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500+ Computer Science Research Topics

Computer Science is a constantly evolving field that has transformed the world we live in today. With new technologies emerging every day, there are countless research opportunities in this field. Whether you are interested in artificial intelligence, machine learning, cybersecurity, data analytics, or computer networks, there are endless possibilities to explore. In this post, we will delve into some of the most interesting and important research topics in Computer Science. From the latest advancements in programming languages to the development of cutting-edge algorithms, we will explore the latest trends and innovations that are shaping the future of Computer Science. So, whether you are a student or a professional, read on to discover some of the most exciting research topics in this dynamic and rapidly expanding field.

Computer Science Research Topics

Computer Science Research Topics are as follows:

Using machine learning to detect and prevent cyber attacks
Developing algorithms for optimized resource allocation in cloud computing
Investigating the use of blockchain technology for secure and decentralized data storage
Developing intelligent chatbots for customer service
Investigating the effectiveness of deep learning for natural language processing
Developing algorithms for detecting and removing fake news from social media
Investigating the impact of social media on mental health
Developing algorithms for efficient image and video compression
Investigating the use of big data analytics for predictive maintenance in manufacturing
Developing algorithms for identifying and mitigating bias in machine learning models
Investigating the ethical implications of autonomous vehicles
Developing algorithms for detecting and preventing cyberbullying
Investigating the use of machine learning for personalized medicine
Developing algorithms for efficient and accurate speech recognition
Investigating the impact of social media on political polarization
Developing algorithms for sentiment analysis in social media data
Investigating the use of virtual reality in education
Developing algorithms for efficient data encryption and decryption
Investigating the impact of technology on workplace productivity
Developing algorithms for detecting and mitigating deepfakes
Investigating the use of artificial intelligence in financial trading
Developing algorithms for efficient database management
Investigating the effectiveness of online learning platforms
Developing algorithms for efficient and accurate facial recognition
Investigating the use of machine learning for predicting weather patterns
Developing algorithms for efficient and secure data transfer
Investigating the impact of technology on social skills and communication
Developing algorithms for efficient and accurate object recognition
Investigating the use of machine learning for fraud detection in finance
Developing algorithms for efficient and secure authentication systems
Investigating the impact of technology on privacy and surveillance
Developing algorithms for efficient and accurate handwriting recognition
Investigating the use of machine learning for predicting stock prices
Developing algorithms for efficient and secure biometric identification
Investigating the impact of technology on mental health and well-being
Developing algorithms for efficient and accurate language translation
Investigating the use of machine learning for personalized advertising
Developing algorithms for efficient and secure payment systems
Investigating the impact of technology on the job market and automation
Developing algorithms for efficient and accurate object tracking
Investigating the use of machine learning for predicting disease outbreaks
Developing algorithms for efficient and secure access control
Investigating the impact of technology on human behavior and decision making
Developing algorithms for efficient and accurate sound recognition
Investigating the use of machine learning for predicting customer behavior
Developing algorithms for efficient and secure data backup and recovery
Investigating the impact of technology on education and learning outcomes
Developing algorithms for efficient and accurate emotion recognition
Investigating the use of machine learning for improving healthcare outcomes
Developing algorithms for efficient and secure supply chain management
Investigating the impact of technology on cultural and societal norms
Developing algorithms for efficient and accurate gesture recognition
Investigating the use of machine learning for predicting consumer demand
Developing algorithms for efficient and secure cloud storage
Investigating the impact of technology on environmental sustainability
Developing algorithms for efficient and accurate voice recognition
Investigating the use of machine learning for improving transportation systems
Developing algorithms for efficient and secure mobile device management
Investigating the impact of technology on social inequality and access to resources
Machine learning for healthcare diagnosis and treatment
Machine Learning for Cybersecurity
Machine learning for personalized medicine
Cybersecurity threats and defense strategies
Big data analytics for business intelligence
Blockchain technology and its applications
Human-computer interaction in virtual reality environments
Artificial intelligence for autonomous vehicles
Natural language processing for chatbots
Cloud computing and its impact on the IT industry
Internet of Things (IoT) and smart homes
Robotics and automation in manufacturing
Augmented reality and its potential in education
Data mining techniques for customer relationship management
Computer vision for object recognition and tracking
Quantum computing and its applications in cryptography
Social media analytics and sentiment analysis
Recommender systems for personalized content delivery
Mobile computing and its impact on society
Bioinformatics and genomic data analysis
Deep learning for image and speech recognition
Digital signal processing and audio processing algorithms
Cloud storage and data security in the cloud
Wearable technology and its impact on healthcare
Computational linguistics for natural language understanding
Cognitive computing for decision support systems
Cyber-physical systems and their applications
Edge computing and its impact on IoT
Machine learning for fraud detection
Cryptography and its role in secure communication
Cybersecurity risks in the era of the Internet of Things
Natural language generation for automated report writing
3D printing and its impact on manufacturing
Virtual assistants and their applications in daily life
Cloud-based gaming and its impact on the gaming industry
Computer networks and their security issues
Cyber forensics and its role in criminal investigations
Machine learning for predictive maintenance in industrial settings
Augmented reality for cultural heritage preservation
Human-robot interaction and its applications
Data visualization and its impact on decision-making
Cybersecurity in financial systems and blockchain
Computer graphics and animation techniques
Biometrics and its role in secure authentication
Cloud-based e-learning platforms and their impact on education
Natural language processing for machine translation
Machine learning for predictive maintenance in healthcare
Cybersecurity and privacy issues in social media
Computer vision for medical image analysis
Natural language generation for content creation
Cybersecurity challenges in cloud computing
Human-robot collaboration in manufacturing
Data mining for predicting customer churn
Artificial intelligence for autonomous drones
Cybersecurity risks in the healthcare industry
Machine learning for speech synthesis
Edge computing for low-latency applications
Virtual reality for mental health therapy
Quantum computing and its applications in finance
Biomedical engineering and its applications
Cybersecurity in autonomous systems
Machine learning for predictive maintenance in transportation
Computer vision for object detection in autonomous driving
Augmented reality for industrial training and simulations
Cloud-based cybersecurity solutions for small businesses
Natural language processing for knowledge management
Machine learning for personalized advertising
Cybersecurity in the supply chain management
Cybersecurity risks in the energy sector
Computer vision for facial recognition
Natural language processing for social media analysis
Machine learning for sentiment analysis in customer reviews
Explainable Artificial Intelligence
Quantum Computing
Blockchain Technology
Human-Computer Interaction
Natural Language Processing
Cloud Computing
Robotics and Automation
Augmented Reality and Virtual Reality
Cyber-Physical Systems
Computational Neuroscience
Big Data Analytics
Computer Vision
Cryptography and Network Security
Internet of Things
Computer Graphics and Visualization
Artificial Intelligence for Game Design
Computational Biology
Social Network Analysis
Bioinformatics
Distributed Systems and Middleware
Information Retrieval and Data Mining
Computer Networks
Mobile Computing and Wireless Networks
Software Engineering
Database Systems
Parallel and Distributed Computing
Human-Robot Interaction
Intelligent Transportation Systems
High-Performance Computing
Cyber-Physical Security
Deep Learning
Sensor Networks
Multi-Agent Systems
Human-Centered Computing
Wearable Computing
Knowledge Representation and Reasoning
Adaptive Systems
Brain-Computer Interface
Health Informatics
Cognitive Computing
Cybersecurity and Privacy
Internet Security
Cybercrime and Digital Forensics
Cloud Security
Cryptocurrencies and Digital Payments
Machine Learning for Natural Language Generation
Cognitive Robotics
Neural Networks
Semantic Web
Image Processing
Cyber Threat Intelligence
Secure Mobile Computing
Cybersecurity Education and Training
Privacy Preserving Techniques
Cyber-Physical Systems Security
Virtualization and Containerization
Machine Learning for Computer Vision
Network Function Virtualization
Cybersecurity Risk Management
Information Security Governance
Intrusion Detection and Prevention
Biometric Authentication
Machine Learning for Predictive Maintenance
Security in Cloud-based Environments
Cybersecurity for Industrial Control Systems
Smart Grid Security
Software Defined Networking
Quantum Cryptography
Security in the Internet of Things
Natural language processing for sentiment analysis
Blockchain technology for secure data sharing
Developing efficient algorithms for big data analysis
Cybersecurity for internet of things (IoT) devices
Human-robot interaction for industrial automation
Image recognition for autonomous vehicles
Social media analytics for marketing strategy
Quantum computing for solving complex problems
Biometric authentication for secure access control
Augmented reality for education and training
Intelligent transportation systems for traffic management
Predictive modeling for financial markets
Cloud computing for scalable data storage and processing
Virtual reality for therapy and mental health treatment
Data visualization for business intelligence
Recommender systems for personalized product recommendations
Speech recognition for voice-controlled devices
Mobile computing for real-time location-based services
Neural networks for predicting user behavior
Genetic algorithms for optimization problems
Distributed computing for parallel processing
Internet of things (IoT) for smart cities
Wireless sensor networks for environmental monitoring
Cloud-based gaming for high-performance gaming
Social network analysis for identifying influencers
Autonomous systems for agriculture
Robotics for disaster response
Data mining for customer segmentation
Computer graphics for visual effects in movies and video games
Virtual assistants for personalized customer service
Natural language understanding for chatbots
3D printing for manufacturing prototypes
Artificial intelligence for stock trading
Machine learning for weather forecasting
Biomedical engineering for prosthetics and implants
Cybersecurity for financial institutions
Machine learning for energy consumption optimization
Computer vision for object tracking
Natural language processing for document summarization
Wearable technology for health and fitness monitoring
Internet of things (IoT) for home automation
Reinforcement learning for robotics control
Big data analytics for customer insights
Machine learning for supply chain optimization
Natural language processing for legal document analysis
Artificial intelligence for drug discovery
Computer vision for object recognition in robotics
Data mining for customer churn prediction
Autonomous systems for space exploration
Robotics for agriculture automation
Machine learning for predicting earthquakes
Natural language processing for sentiment analysis in customer reviews
Big data analytics for predicting natural disasters
Internet of things (IoT) for remote patient monitoring
Blockchain technology for digital identity management
Machine learning for predicting wildfire spread
Computer vision for gesture recognition
Natural language processing for automated translation
Big data analytics for fraud detection in banking
Internet of things (IoT) for smart homes
Robotics for warehouse automation
Machine learning for predicting air pollution
Natural language processing for medical record analysis
Augmented reality for architectural design
Big data analytics for predicting traffic congestion
Machine learning for predicting customer lifetime value
Developing algorithms for efficient and accurate text recognition
Natural Language Processing for Virtual Assistants
Natural Language Processing for Sentiment Analysis in Social Media
Explainable Artificial Intelligence (XAI) for Trust and Transparency
Deep Learning for Image and Video Retrieval
Edge Computing for Internet of Things (IoT) Applications
Data Science for Social Media Analytics
Cybersecurity for Critical Infrastructure Protection
Natural Language Processing for Text Classification
Quantum Computing for Optimization Problems
Machine Learning for Personalized Health Monitoring
Computer Vision for Autonomous Driving
Blockchain Technology for Supply Chain Management
Augmented Reality for Education and Training
Natural Language Processing for Sentiment Analysis
Machine Learning for Personalized Marketing
Big Data Analytics for Financial Fraud Detection
Cybersecurity for Cloud Security Assessment
Artificial Intelligence for Natural Language Understanding
Blockchain Technology for Decentralized Applications
Virtual Reality for Cultural Heritage Preservation
Natural Language Processing for Named Entity Recognition
Machine Learning for Customer Churn Prediction
Big Data Analytics for Social Network Analysis
Cybersecurity for Intrusion Detection and Prevention
Artificial Intelligence for Robotics and Automation
Blockchain Technology for Digital Identity Management
Virtual Reality for Rehabilitation and Therapy
Natural Language Processing for Text Summarization
Machine Learning for Credit Risk Assessment
Big Data Analytics for Fraud Detection in Healthcare
Cybersecurity for Internet Privacy Protection
Artificial Intelligence for Game Design and Development
Blockchain Technology for Decentralized Social Networks
Virtual Reality for Marketing and Advertising
Natural Language Processing for Opinion Mining
Machine Learning for Anomaly Detection
Big Data Analytics for Predictive Maintenance in Transportation
Cybersecurity for Network Security Management
Artificial Intelligence for Personalized News and Content Delivery
Blockchain Technology for Cryptocurrency Mining
Virtual Reality for Architectural Design and Visualization
Natural Language Processing for Machine Translation
Machine Learning for Automated Image Captioning
Big Data Analytics for Stock Market Prediction
Cybersecurity for Biometric Authentication Systems
Artificial Intelligence for Human-Robot Interaction
Blockchain Technology for Smart Grids
Virtual Reality for Sports Training and Simulation
Natural Language Processing for Question Answering Systems
Machine Learning for Sentiment Analysis in Customer Feedback
Big Data Analytics for Predictive Maintenance in Manufacturing
Cybersecurity for Cloud-Based Systems
Artificial Intelligence for Automated Journalism
Blockchain Technology for Intellectual Property Management
Virtual Reality for Therapy and Rehabilitation
Natural Language Processing for Language Generation
Machine Learning for Customer Lifetime Value Prediction
Big Data Analytics for Predictive Maintenance in Energy Systems
Cybersecurity for Secure Mobile Communication
Artificial Intelligence for Emotion Recognition
Blockchain Technology for Digital Asset Trading
Virtual Reality for Automotive Design and Visualization
Natural Language Processing for Semantic Web
Machine Learning for Fraud Detection in Financial Transactions
Big Data Analytics for Social Media Monitoring
Cybersecurity for Cloud Storage and Sharing
Artificial Intelligence for Personalized Education
Blockchain Technology for Secure Online Voting Systems
Virtual Reality for Cultural Tourism
Natural Language Processing for Chatbot Communication
Machine Learning for Medical Diagnosis and Treatment
Big Data Analytics for Environmental Monitoring and Management.
Cybersecurity for Cloud Computing Environments
Virtual Reality for Training and Simulation
Big Data Analytics for Sports Performance Analysis
Cybersecurity for Internet of Things (IoT) Devices
Artificial Intelligence for Traffic Management and Control
Blockchain Technology for Smart Contracts
Natural Language Processing for Document Summarization
Machine Learning for Image and Video Recognition
Blockchain Technology for Digital Asset Management
Virtual Reality for Entertainment and Gaming
Natural Language Processing for Opinion Mining in Online Reviews
Machine Learning for Customer Relationship Management
Big Data Analytics for Environmental Monitoring and Management
Cybersecurity for Network Traffic Analysis and Monitoring
Artificial Intelligence for Natural Language Generation
Blockchain Technology for Supply Chain Transparency and Traceability
Virtual Reality for Design and Visualization
Natural Language Processing for Speech Recognition
Machine Learning for Recommendation Systems
Big Data Analytics for Customer Segmentation and Targeting
Cybersecurity for Biometric Authentication
Artificial Intelligence for Human-Computer Interaction
Blockchain Technology for Decentralized Finance (DeFi)
Virtual Reality for Tourism and Cultural Heritage
Machine Learning for Cybersecurity Threat Detection and Prevention
Big Data Analytics for Healthcare Cost Reduction
Cybersecurity for Data Privacy and Protection
Artificial Intelligence for Autonomous Vehicles
Blockchain Technology for Cryptocurrency and Blockchain Security
Virtual Reality for Real Estate Visualization
Natural Language Processing for Question Answering
Big Data Analytics for Financial Markets Prediction
Cybersecurity for Cloud-Based Machine Learning Systems
Artificial Intelligence for Personalized Advertising
Blockchain Technology for Digital Identity Verification
Virtual Reality for Cultural and Language Learning
Natural Language Processing for Semantic Analysis
Machine Learning for Business Forecasting
Big Data Analytics for Social Media Marketing
Artificial Intelligence for Content Generation
Blockchain Technology for Smart Cities
Virtual Reality for Historical Reconstruction
Natural Language Processing for Knowledge Graph Construction
Machine Learning for Speech Synthesis
Big Data Analytics for Traffic Optimization
Artificial Intelligence for Social Robotics
Blockchain Technology for Healthcare Data Management
Virtual Reality for Disaster Preparedness and Response
Natural Language Processing for Multilingual Communication
Machine Learning for Emotion Recognition
Big Data Analytics for Human Resources Management
Cybersecurity for Mobile App Security
Artificial Intelligence for Financial Planning and Investment
Blockchain Technology for Energy Management
Virtual Reality for Cultural Preservation and Heritage.
Big Data Analytics for Healthcare Management
Cybersecurity in the Internet of Things (IoT)
Artificial Intelligence for Predictive Maintenance
Computational Biology for Drug Discovery
Virtual Reality for Mental Health Treatment
Machine Learning for Sentiment Analysis in Social Media
Human-Computer Interaction for User Experience Design
Cloud Computing for Disaster Recovery
Quantum Computing for Cryptography
Intelligent Transportation Systems for Smart Cities
Cybersecurity for Autonomous Vehicles
Artificial Intelligence for Fraud Detection in Financial Systems
Social Network Analysis for Marketing Campaigns
Cloud Computing for Video Game Streaming
Machine Learning for Speech Recognition
Augmented Reality for Architecture and Design
Natural Language Processing for Customer Service Chatbots
Machine Learning for Climate Change Prediction
Big Data Analytics for Social Sciences
Artificial Intelligence for Energy Management
Virtual Reality for Tourism and Travel
Cybersecurity for Smart Grids
Machine Learning for Image Recognition
Augmented Reality for Sports Training
Natural Language Processing for Content Creation
Cloud Computing for High-Performance Computing
Artificial Intelligence for Personalized Medicine
Virtual Reality for Architecture and Design
Augmented Reality for Product Visualization
Natural Language Processing for Language Translation
Cybersecurity for Cloud Computing
Artificial Intelligence for Supply Chain Optimization
Blockchain Technology for Digital Voting Systems
Virtual Reality for Job Training
Augmented Reality for Retail Shopping
Natural Language Processing for Sentiment Analysis in Customer Feedback
Cloud Computing for Mobile Application Development
Artificial Intelligence for Cybersecurity Threat Detection
Blockchain Technology for Intellectual Property Protection
Virtual Reality for Music Education
Machine Learning for Financial Forecasting
Augmented Reality for Medical Education
Natural Language Processing for News Summarization
Cybersecurity for Healthcare Data Protection
Artificial Intelligence for Autonomous Robots
Virtual Reality for Fitness and Health
Machine Learning for Natural Language Understanding
Augmented Reality for Museum Exhibits
Natural Language Processing for Chatbot Personality Development
Cloud Computing for Website Performance Optimization
Artificial Intelligence for E-commerce Recommendation Systems
Blockchain Technology for Supply Chain Traceability
Virtual Reality for Military Training
Augmented Reality for Advertising
Natural Language Processing for Chatbot Conversation Management
Cybersecurity for Cloud-Based Services
Artificial Intelligence for Agricultural Management
Blockchain Technology for Food Safety Assurance
Virtual Reality for Historical Reenactments
Machine Learning for Cybersecurity Incident Response.
Secure Multiparty Computation
Federated Learning
Internet of Things Security
Blockchain Scalability
Quantum Computing Algorithms
Explainable AI
Data Privacy in the Age of Big Data
Adversarial Machine Learning
Deep Reinforcement Learning
Online Learning and Streaming Algorithms
Graph Neural Networks
Automated Debugging and Fault Localization
Mobile Application Development
Software Engineering for Cloud Computing
Cryptocurrency Security
Edge Computing for Real-Time Applications
Natural Language Generation
Virtual and Augmented Reality
Computational Biology and Bioinformatics
Internet of Things Applications
Robotics and Autonomous Systems
Explainable Robotics
3D Printing and Additive Manufacturing
Distributed Systems
Parallel Computing
Data Center Networking
Data Mining and Knowledge Discovery
Information Retrieval and Search Engines
Network Security and Privacy
Cloud Computing Security
Data Analytics for Business Intelligence
Neural Networks and Deep Learning
Reinforcement Learning for Robotics
Automated Planning and Scheduling
Evolutionary Computation and Genetic Algorithms
Formal Methods for Software Engineering
Computational Complexity Theory
Bio-inspired Computing
Computer Vision for Object Recognition
Automated Reasoning and Theorem Proving
Natural Language Understanding
Machine Learning for Healthcare
Scalable Distributed Systems
Sensor Networks and Internet of Things
Smart Grids and Energy Systems
Software Testing and Verification
Web Application Security
Wireless and Mobile Networks
Computer Architecture and Hardware Design
Digital Signal Processing
Game Theory and Mechanism Design
Multi-agent Systems
Evolutionary Robotics
Quantum Machine Learning
Computational Social Science
Explainable Recommender Systems.
Artificial Intelligence and its applications
Cloud computing and its benefits
Cybersecurity threats and solutions
Internet of Things and its impact on society
Virtual and Augmented Reality and its uses
Blockchain Technology and its potential in various industries
Web Development and Design
Digital Marketing and its effectiveness
Big Data and Analytics
Software Development Life Cycle
Gaming Development and its growth
Network Administration and Maintenance
Machine Learning and its uses
Data Warehousing and Mining
Computer Architecture and Design
Computer Graphics and Animation
Quantum Computing and its potential
Data Structures and Algorithms
Computer Vision and Image Processing
Robotics and its applications
Operating Systems and its functions
Information Theory and Coding
Compiler Design and Optimization
Computer Forensics and Cyber Crime Investigation
Distributed Computing and its significance
Artificial Neural Networks and Deep Learning
Cloud Storage and Backup
Programming Languages and their significance
Computer Simulation and Modeling
Computer Networks and its types
Information Security and its types
Computer-based Training and eLearning
Medical Imaging and its uses
Social Media Analysis and its applications
Human Resource Information Systems
Computer-Aided Design and Manufacturing
Multimedia Systems and Applications
Geographic Information Systems and its uses
Computer-Assisted Language Learning
Mobile Device Management and Security
Data Compression and its types
Knowledge Management Systems
Text Mining and its uses
Cyber Warfare and its consequences
Wireless Networks and its advantages
Computer Ethics and its importance
Computational Linguistics and its applications
Autonomous Systems and Robotics
Information Visualization and its importance
Geographic Information Retrieval and Mapping
Business Intelligence and its benefits
Digital Libraries and their significance
Artificial Life and Evolutionary Computation
Computer Music and its types
Virtual Teams and Collaboration
Computer Games and Learning
Semantic Web and its applications
Electronic Commerce and its advantages
Multimedia Databases and their significance
Computer Science Education and its importance
Computer-Assisted Translation and Interpretation
Ambient Intelligence and Smart Homes
Autonomous Agents and Multi-Agent Systems.

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Research Topics & Ideas: CompSci & IT

50+ Computer Science Research Topic Ideas To Fast-Track Your Project

Finding and choosing a strong research topic is the critical first step when it comes to crafting a high-quality dissertation, thesis or research project. If you’ve landed on this post, chances are you’re looking for a computer science-related research topic , but aren’t sure where to start. Here, we’ll explore a variety of CompSci & IT-related research ideas and topic thought-starters, including algorithms, AI, networking, database systems, UX, information security and software engineering.

NB – This is just the start…

The topic ideation and evaluation process has multiple steps . In this post, we’ll kickstart the process by sharing some research topic ideas within the CompSci domain. This is the starting point, but to develop a well-defined research topic, you’ll need to identify a clear and convincing research gap , along with a well-justified plan of action to fill that gap.

If you’re new to the oftentimes perplexing world of research, or if this is your first time undertaking a formal academic research project, be sure to check out our free dissertation mini-course. In it, we cover the process of writing a dissertation or thesis from start to end. Be sure to also sign up for our free webinar that explores how to find a high-quality research topic.

Overview: CompSci Research Topics

Algorithms & data structures
Artificial intelligence ( AI )
Computer networking
Database systems
Human-computer interaction
Information security (IS)
Software engineering
Examples of CompSci dissertation & theses

Topics/Ideas: Algorithms & Data Structures

An analysis of neural network algorithms’ accuracy for processing consumer purchase patterns
A systematic review of the impact of graph algorithms on data analysis and discovery in social media network analysis
An evaluation of machine learning algorithms used for recommender systems in streaming services
A review of approximation algorithm approaches for solving NP-hard problems
An analysis of parallel algorithms for high-performance computing of genomic data
The influence of data structures on optimal algorithm design and performance in Fintech
A Survey of algorithms applied in internet of things (IoT) systems in supply-chain management
A comparison of streaming algorithm performance for the detection of elephant flows
A systematic review and evaluation of machine learning algorithms used in facial pattern recognition
Exploring the performance of a decision tree-based approach for optimizing stock purchase decisions
Assessing the importance of complete and representative training datasets in Agricultural machine learning based decision making.
A Comparison of Deep learning algorithms performance for structured and unstructured datasets with “rare cases”
A systematic review of noise reduction best practices for machine learning algorithms in geoinformatics.
Exploring the feasibility of applying information theory to feature extraction in retail datasets.
Assessing the use case of neural network algorithms for image analysis in biodiversity assessment

Topics & Ideas: Artificial Intelligence (AI)

Applying deep learning algorithms for speech recognition in speech-impaired children
A review of the impact of artificial intelligence on decision-making processes in stock valuation
An evaluation of reinforcement learning algorithms used in the production of video games
An exploration of key developments in natural language processing and how they impacted the evolution of Chabots.
An analysis of the ethical and social implications of artificial intelligence-based automated marking
The influence of large-scale GIS datasets on artificial intelligence and machine learning developments
An examination of the use of artificial intelligence in orthopaedic surgery
The impact of explainable artificial intelligence (XAI) on transparency and trust in supply chain management
An evaluation of the role of artificial intelligence in financial forecasting and risk management in cryptocurrency
A meta-analysis of deep learning algorithm performance in predicting and cyber attacks in schools

Topics & Ideas: Networking

An analysis of the impact of 5G technology on internet penetration in rural Tanzania
Assessing the role of software-defined networking (SDN) in modern cloud-based computing
A critical analysis of network security and privacy concerns associated with Industry 4.0 investment in healthcare.
Exploring the influence of cloud computing on security risks in fintech.
An examination of the use of network function virtualization (NFV) in telecom networks in Southern America
Assessing the impact of edge computing on network architecture and design in IoT-based manufacturing
An evaluation of the challenges and opportunities in 6G wireless network adoption
The role of network congestion control algorithms in improving network performance on streaming platforms
An analysis of network coding-based approaches for data security
Assessing the impact of network topology on network performance and reliability in IoT-based workspaces

Free Webinar: How To Find A Dissertation Research Topic

Topics & Ideas: Database Systems

An analysis of big data management systems and technologies used in B2B marketing
The impact of NoSQL databases on data management and analysis in smart cities
An evaluation of the security and privacy concerns of cloud-based databases in financial organisations
Exploring the role of data warehousing and business intelligence in global consultancies
An analysis of the use of graph databases for data modelling and analysis in recommendation systems
The influence of the Internet of Things (IoT) on database design and management in the retail grocery industry
An examination of the challenges and opportunities of distributed databases in supply chain management
Assessing the impact of data compression algorithms on database performance and scalability in cloud computing
An evaluation of the use of in-memory databases for real-time data processing in patient monitoring
Comparing the effects of database tuning and optimization approaches in improving database performance and efficiency in omnichannel retailing

Topics & Ideas: Human-Computer Interaction

An analysis of the impact of mobile technology on human-computer interaction prevalence in adolescent men
An exploration of how artificial intelligence is changing human-computer interaction patterns in children
An evaluation of the usability and accessibility of web-based systems for CRM in the fast fashion retail sector
Assessing the influence of virtual and augmented reality on consumer purchasing patterns
An examination of the use of gesture-based interfaces in architecture
Exploring the impact of ease of use in wearable technology on geriatric user
Evaluating the ramifications of gamification in the Metaverse
A systematic review of user experience (UX) design advances associated with Augmented Reality
A comparison of natural language processing algorithms automation of customer response Comparing end-user perceptions of natural language processing algorithms for automated customer response
Analysing the impact of voice-based interfaces on purchase practices in the fast food industry

Research Topic Kickstarter - Need Help Finding A Research Topic?

Topics & Ideas: Information Security

A bibliometric review of current trends in cryptography for secure communication
An analysis of secure multi-party computation protocols and their applications in cloud-based computing
An investigation of the security of blockchain technology in patient health record tracking
A comparative study of symmetric and asymmetric encryption algorithms for instant text messaging
A systematic review of secure data storage solutions used for cloud computing in the fintech industry
An analysis of intrusion detection and prevention systems used in the healthcare sector
Assessing security best practices for IoT devices in political offices
An investigation into the role social media played in shifting regulations related to privacy and the protection of personal data
A comparative study of digital signature schemes adoption in property transfers
An assessment of the security of secure wireless communication systems used in tertiary institutions

Topics & Ideas: Software Engineering

A study of agile software development methodologies and their impact on project success in pharmacology
Investigating the impacts of software refactoring techniques and tools in blockchain-based developments
A study of the impact of DevOps practices on software development and delivery in the healthcare sector
An analysis of software architecture patterns and their impact on the maintainability and scalability of cloud-based offerings
A study of the impact of artificial intelligence and machine learning on software engineering practices in the education sector
An investigation of software testing techniques and methodologies for subscription-based offerings
A review of software security practices and techniques for protecting against phishing attacks from social media
An analysis of the impact of cloud computing on the rate of software development and deployment in the manufacturing sector
Exploring the impact of software development outsourcing on project success in multinational contexts
An investigation into the effect of poor software documentation on app success in the retail sector

CompSci & IT Dissertations/Theses

While the ideas we’ve presented above are a decent starting point for finding a CompSci-related research topic, they are fairly generic and non-specific. So, it helps to look at actual dissertations and theses to see how this all comes together.

Below, we’ve included a selection of research projects from various CompSci-related degree programs to help refine your thinking. These are actual dissertations and theses, written as part of Master’s and PhD-level programs, so they can provide some useful insight as to what a research topic looks like in practice.

An array-based optimization framework for query processing and data analytics (Chen, 2021)
Dynamic Object Partitioning and replication for cooperative cache (Asad, 2021)
Embedding constructural documentation in unit tests (Nassif, 2019)
PLASA | Programming Language for Synchronous Agents (Kilaru, 2019)
Healthcare Data Authentication using Deep Neural Network (Sekar, 2020)
Virtual Reality System for Planetary Surface Visualization and Analysis (Quach, 2019)
Artificial neural networks to predict share prices on the Johannesburg stock exchange (Pyon, 2021)
Predicting household poverty with machine learning methods: the case of Malawi (Chinyama, 2022)
Investigating user experience and bias mitigation of the multi-modal retrieval of historical data (Singh, 2021)
Detection of HTTPS malware traffic without decryption (Nyathi, 2022)
Redefining privacy: case study of smart health applications (Al-Zyoud, 2019)
A state-based approach to context modeling and computing (Yue, 2019)
A Novel Cooperative Intrusion Detection System for Mobile Ad Hoc Networks (Solomon, 2019)
HRSB-Tree for Spatio-Temporal Aggregates over Moving Regions (Paduri, 2019)

Looking at these titles, you can probably pick up that the research topics here are quite specific and narrowly-focused , compared to the generic ones presented earlier. This is an important thing to keep in mind as you develop your own research topic. That is to say, to create a top-notch research topic, you must be precise and target a specific context with specific variables of interest . In other words, you need to identify a clear, well-justified research gap.

Fast-Track Your Research Topic

If you’re still feeling a bit unsure about how to find a research topic for your Computer Science dissertation or research project, check out our Topic Kickstarter service.

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100 Great Computer Science Research Topics Ideas for 2023

Being a computer student in 2023 is not easy. Besides studying a constantly evolving subject, you have to come up with great computer science research topics at some point in your academic life. If you’re reading this article, you’re among many other students that have also come to this realization.

Interesting Computer Science Topics

Awesome research topics in computer science, hot topics in computer science, topics to publish a journal on computer science.

Controversial Topics in Computer Science

Fun AP Computer Science Topics

Exciting computer science ph.d. topics, remarkable computer science research topics for undergraduates, incredible final year computer science project topics, advanced computer science topics, unique seminars topics for computer science, exceptional computer science masters thesis topics, outstanding computer science presentation topics.

Key Computer Science Essay Topics

Main Project Topics for Computer Science

We Can Help You with Computer Science Topics

Whether you’re earnestly searching for a topic or stumbled onto this article by accident, there is no doubt that every student needs excellent computer science-related topics for their paper. A good topic will not only give your essay or research a good direction but will also make it easy to come up with supporting points. Your topic should show all your strengths as well.

Fortunately, this article is for every student that finds it hard to generate a suitable computer science topic. The following 100+ topics will help give you some inspiration when creating your topics. Let’s get into it.

One of the best ways of making your research paper interesting is by coming up with relevant topics in computer science . Here are some topics that will make your paper immersive:

Evolution of virtual reality
What is green cloud computing
Ways of creating a Hopefield neural network in C++
Developments in graphic systems in computers
The five principal fields in robotics
Developments and applications of nanotechnology
Differences between computer science and applied computing

Your next research topic in computer science shouldn’t be tough to find once you’ve read this section. If you’re looking for simple final year project topics in computer science, you can find some below.

Applications of the blockchain technology in the banking industry
Computational thinking and how it influences science
Ways of terminating phishing
Uses of artificial intelligence in cyber security
Define the concepts of a smart city
Applications of the Internet of Things
Discuss the applications of the face detection application

Whenever a topic is described as “hot,” it means that it is a trendy topic in computer science. If computer science project topics for your final years are what you’re looking for, have a look at some below:

Applications of the Metaverse in the world today
Discuss the challenges of machine learning
Advantages of artificial intelligence
Applications of nanotechnology in the paints industry
What is quantum computing?
Discuss the languages of parallel computing
What are the applications of computer-assisted studies?

Perhaps you’d like to write a paper that will get published in a journal. If you’re searching for the best project topics for computer science students that will stand out in a journal, check below:

Developments in human-computer interaction
Applications of computer science in medicine
Developments in artificial intelligence in image processing
Discuss cryptography and its applications
Discuss methods of ransomware prevention
Applications of Big Data in the banking industry
Challenges of cloud storage services in 2023

Controversial Topics in Computer Science

Some of the best computer science final year project topics are those that elicit debates or require you to take a stand. You can find such topics listed below for your inspiration:

Can robots be too intelligent?
Should the dark web be shut down?
Should your data be sold to corporations?
Will robots completely replace the human workforce one day?
How safe is the Metaverse for children?
Will artificial intelligence replace actors in Hollywood?
Are social media platforms safe anymore?

Are you a computer science student looking for AP topics? You’re in luck because the following final year project topics for computer science are suitable for you.

Standard browser core with CSS support
Applications of the Gaussian method in C++ development in integrating functions
Vital conditions of reducing risk through the Newton method
How to reinforce machine learning algorithms.
How do artificial neural networks function?
Discuss the advancements in computer languages in machine learning
Use of artificial intelligence in automated cars

When studying to get your doctorate in computer science, you need clear and relevant topics that generate the reader’s interest. Here are some Ph.D. topics in computer science you might consider:

Developments in information technology
Is machine learning detrimental to the human workforce?
How to write an algorithm for deep learning
What is the future of 5G in wireless networks
Statistical data in Maths modules in Python
Data retention automation from a website using API
Application of modern programming languages

Looking for computer science topics for research is not easy for an undergraduate. Fortunately, these computer science project topics should make your research paper easy:

Ways of using artificial intelligence in real estate
Discuss reinforcement learning and its applications
Uses of Big Data in science and medicine
How to sort algorithms using Haskell
How to create 3D configurations for a website
Using inverse interpolation to solve non-linear equations
Explain the similarities between the Internet of Things and artificial intelligence

Your dissertation paper is one of the most crucial papers you’ll ever do in your final year. That’s why selecting the best ethics in computer science topics is a crucial part of your paper. Here are some project topics for the computer science final year.

How to incorporate numerical methods in programming
Applications of blockchain technology in cloud storage
How to come up with an automated attendance system
Using dynamic libraries for site development
How to create cubic splines
Applications of artificial intelligence in the stock market
Uses of quantum computing in financial modeling

Your instructor may want you to challenge yourself with an advanced science project. Thus, you may require computer science topics to learn and research. Here are some that may inspire you:

Discuss the best cryptographic protocols
Advancement of artificial intelligence used in smartphones
Briefly discuss the types of security software available
Application of liquid robots in 2023
How to use quantum computers to solve decoherence problem
macOS vs. Windows; discuss their similarities and differences
Explain the steps taken in a cyber security audit

When searching for computer science topics for a seminar, make sure they are based on current research or events. Below are some of the latest research topics in computer science:

How to reduce cyber-attacks in 2023
Steps followed in creating a network
Discuss the uses of data science
Discuss ways in which social robots improve human interactions
Differentiate between supervised and unsupervised machine learning
Applications of robotics in space exploration
The contrast between cyber-physical and sensor network systems

Are you looking for computer science thesis topics for your upcoming projects? The topics below are meant to help you write your best paper yet:

Applications of computer science in sports
Uses of computer technology in the electoral process
Using Fibonacci to solve the functions maximum and their implementations
Discuss the advantages of using open-source software
Expound on the advancement of computer graphics
Briefly discuss the uses of mesh generation in computational domains
How much data is generated from the internet of things?

A computer science presentation requires a topic relevant to current events. Whether your paper is an assignment or a dissertation, you can find your final year computer science project topics below:

Uses of adaptive learning in the financial industry
Applications of transitive closure on graph
Using RAD technology in developing software
Discuss how to create maximum flow in the network
How to design and implement functional mapping
Using artificial intelligence in courier tracking and deliveries
How to make an e-authentication system

Key Computer Science Essay Topics

You may be pressed for time and require computer science master thesis topics that are easy. Below are some topics that fit this description:

What are the uses of cloud computing in 2023
Discuss the server-side web technologies
Compare and contrast android and iOS
How to come up with a face detection algorithm
What is the future of NFTs
How to create an artificial intelligence shopping system
How to make a software piracy prevention algorithm

One major mistake students make when writing their papers is selecting topics unrelated to the study at hand. This, however, will not be an issue if you get topics related to computer science, such as the ones below:

Using blockchain to create a supply chain management system
How to protect a web app from malicious attacks
Uses of distributed information processing systems
Advancement of crowd communication software since COVID-19
Uses of artificial intelligence in online casinos
Discuss the pillars of math computations
Discuss the ethical concerns arising from data mining

We Can Help You with Computer Science Topics, Essays, Thesis, and Research Papers

We hope that this list of computer science topics helps you out of your sticky situation. We do offer other topics in different subjects. Additionally, we also offer professional writing services tailor-made for you.

We understand what students go through when searching the internet for computer science research paper topics, and we know that many students don’t know how to write a research paper to perfection. However, you shouldn’t have to go through all this when we’re here to help.

Don’t waste any more time; get in touch with us today and get your paper done excellently.

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Computer science is the study and development of the protocols required for automated processing and manipulation of data. This includes, for example, creating algorithms for efficiently searching large volumes of information or encrypting data so that it can be stored and transmitted securely.

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The Top 10 Most Interesting Computer Science Research Topics

Computer science touches nearly every area of our lives. With new advancements in technology, the computer science field is constantly evolving, giving rise to new computer science research topics. These topics attempt to answer various computer science research questions and how they affect the tech industry and the larger world.

Computer science research topics can be divided into several categories, such as artificial intelligence, big data and data science, human-computer interaction, security and privacy, and software engineering. If you are a student or researcher looking for computer research paper topics. In that case, this article provides some suggestions on examples of computer science research topics and questions.

Find your bootcamp match

What makes a strong computer science research topic.

A strong computer science topic is clear, well-defined, and easy to understand. It should also reflect the research’s purpose, scope, or aim. In addition, a strong computer science research topic is devoid of abbreviations that are not generally known, though, it can include industry terms that are currently and generally accepted.

Tips for Choosing a Computer Science Research Topic

Brainstorm . Brainstorming helps you develop a few different ideas and find the best topic for you. Some core questions you should ask are, What are some open questions in computer science? What do you want to learn more about? What are some current trends in computer science?
Choose a sub-field . There are many subfields and career paths in computer science . Before choosing a research topic, ensure that you point out which aspect of computer science the research will focus on. That could be theoretical computer science, contemporary computing culture, or even distributed computing research topics.
Aim to answer a question . When you’re choosing a research topic in computer science, you should always have a question in mind that you’d like to answer. That helps you narrow down your research aim to meet specified clear goals.
Do a comprehensive literature review . When starting a research project, it is essential to have a clear idea of the topic you plan to study. That involves doing a comprehensive literature review to better understand what has been learned about your topic in the past.
Keep the topic simple and clear. The topic should reflect the scope and aim of the research it addresses. It should also be concise and free of ambiguous words. Hence, some researchers recommended that the topic be limited to five to 15 substantive words. It can take the form of a question or a declarative statement.

What’s the Difference Between a Research Topic and a Research Question?

A research topic is the subject matter that a researcher chooses to investigate. You may also refer to it as the title of a research paper. It summarizes the scope of the research and captures the researcher’s approach to the research question. Hence, it may be broad or more specific. For example, a broad topic may read, Data Protection and Blockchain, while a more specific variant can read, Potential Strategies to Privacy Issues on the Blockchain.

On the other hand, a research question is the fundamental starting point for any research project. It typically reflects various real-world problems and, sometimes, theoretical computer science challenges. As such, it must be clear, concise, and answerable.

How to Create Strong Computer Science Research Questions

To create substantial computer science research questions, one must first understand the topic at hand. Furthermore, the research question should generate new knowledge and contribute to the advancement of the field. It could be something that has not been answered before or is only partially answered. It is also essential to consider the feasibility of answering the question.

2. The Influence of Extraction Methods on Big Data Mining

Data mining has drawn the scientific community’s attention, especially with the explosive rise of big data. Many research results prove that the extraction methods used have a significant effect on the outcome of the data mining process. However, a topic like this analyzes algorithms. It suggests strategies and efficient algorithms that may help understand the challenge or lead the way to find a solution.

3. Integration of 5G with Analytics and Artificial Intelligence

According to the International Finance Corporation, 5G and AI technologies are defining emerging markets and our world. Through different technologies, this research aims to find novel ways to integrate these powerful tools to produce excellent results. Subjects like this often spark great discoveries that pioneer new levels of research and innovation. A breakthrough can influence advanced educational technology, virtual reality, metaverse, and medical imaging.

4. Leveraging Asynchronous FPGAs for Crypto Acceleration

To support the growing cryptocurrency industry, there is a need to create new ways to accelerate transaction processing. This project aims to use asynchronous Field-Programmable Gate Arrays (FPGAs) to accelerate cryptocurrency transaction processing. It explores how various distributed computing technologies can influence mining cryptocurrencies faster with FPGAs and generally enjoy faster transactions.

5. Cyber Security Future Technologies

Cyber security is a trending topic among businesses and individuals, especially as many work teams are going remote. Research like this can stretch the length and breadth of the cyber security and cloud security industries and project innovations depending on the researcher’s preferences. Another angle is to analyze existing or emerging solutions and present discoveries that can aid future research.

6. Exploring the Boundaries Between Art, Media, and Information Technology

The field of computers and media is a vast and complex one that intersects in many ways. They create images or animations using design technology like algorithmic mechanism design, design thinking, design theory, digital fabrication systems, and electronic design automation. This paper aims to define how both fields exist independently and symbiotically.

7. Evolution of Future Wireless Networks Using Cognitive Radio Networks

This research project aims to study how cognitive radio technology can drive evolution in future wireless networks. It will analyze the performance of cognitive radio-based wireless networks in different scenarios and measure its impact on spectral efficiency and network capacity. The research project will involve the development of a simulation model for studying the performance of cognitive radios in different scenarios.

8. The Role of Quantum Computing and Machine Learning in Advancing Medical Predictive Systems

In a paper titled Exploring Quantum Computing Use Cases for Healthcare , experts at IBM highlighted precision medicine and diagnostics to benefit from quantum computing. Using biomedical imaging, machine learning, computational biology, and data-intensive computing systems, researchers can create more accurate disease progression prediction, disease severity classification systems, and 3D Image reconstruction systems vital for treating chronic diseases.

9. Implementing Privacy and Security in Wireless Networks

Wireless networks are prone to attacks, and that has been a big concern for both individual users and organizations. According to the Cyber Security and Infrastructure Security Agency CISA, cyber security specialists are working to find reliable methods of securing wireless networks . This research aims to develop a secure and privacy-preserving communication framework for wireless communication and social networks.

10. Exploring the Challenges and Potentials of Biometric Systems Using Computational Techniques

Much discussion surrounds biometric systems and the potential for misuse and privacy concerns. When exploring how biometric systems can be effectively used, issues such as verification time and cost, hygiene, data bias, and cultural acceptance must be weighed. The paper may take a critical study into the various challenges using computational tools and predict possible solutions.

Computer Research Questions

Why are there so many programming languages?
Is there a better way to enhance human-computer interactions in computer-aided learning?
How safe is cloud computing, and what are some ways to enhance security?
Can computers effectively assist in the sequencing of human genes?
How valuable is SCRUM methodology in Agile software development?

Choosing the Right Computer Science Research Topic

Computer science research is a vast field, and it can be challenging to choose the right topic. There are a few things to keep in mind when making this decision. Choose a topic that you are interested in. This will make it easier to stay motivated and produce high-quality research for your computer science degree .

Select a topic that is relevant to your field of study. This will help you to develop specialized knowledge in the area. Choose a topic that has potential for future research. This will ensure that your research is relevant and up-to-date. Typically, coding bootcamps provide a framework that streamlines students’ projects to a specific field, doing their search for a creative solution more effortless.

Computer Science Research Topics FAQ

To start a computer science research project, you should look at what other content is out there. Complete a literature review to know the available findings surrounding your idea. Design your research and ensure that you have the necessary skills and resources to complete the project.

The first step to conducting computer science research is to conceptualize the idea and review existing knowledge about that subject. You will design your research and collect data through surveys or experiments. Analyze your data and build a prototype or graphical model. You will also write a report and present it to a recognized body for review and publication.

You can find computer science research jobs on the job boards of many universities. Many universities have job boards on their websites that list open positions in research and academia. Also, many Slack and GitHub channels for computer scientists provide regular updates on available projects.

There are several hot topics and questions in AI that you can build your research on. Below are some AI research questions you may consider for your research paper.

Will it be possible to build artificial emotional intelligence?
Will robots replace humans in all difficult cumbersome jobs as part of the progress of civilization?
Can artificial intelligence systems self-improve with knowledge from the Internet?

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cs.AI - Artificial Intelligence ( new , recent , current month ) Covers all areas of AI except Vision, Robotics, Machine Learning, Multiagent Systems, and Computation and Language (Natural Language Processing), which have separate subject areas. In particular, includes Expert Systems, Theorem Proving (although this may overlap with Logic in Computer Science), Knowledge Representation, Planning, and Uncertainty in AI. Roughly includes material in ACM Subject Classes I.2.0, I.2.1, I.2.3, I.2.4, I.2.8, and I.2.11.
cs.CL - Computation and Language ( new , recent , current month ) Covers natural language processing. Roughly includes material in ACM Subject Class I.2.7. Note that work on artificial languages (programming languages, logics, formal systems) that does not explicitly address natural-language issues broadly construed (natural-language processing, computational linguistics, speech, text retrieval, etc.) is not appropriate for this area.
cs.CC - Computational Complexity ( new , recent , current month ) Covers models of computation, complexity classes, structural complexity, complexity tradeoffs, upper and lower bounds. Roughly includes material in ACM Subject Classes F.1 (computation by abstract devices), F.2.3 (tradeoffs among complexity measures), and F.4.3 (formal languages), although some material in formal languages may be more appropriate for Logic in Computer Science. Some material in F.2.1 and F.2.2, may also be appropriate here, but is more likely to have Data Structures and Algorithms as the primary subject area.
cs.CE - Computational Engineering, Finance, and Science ( new , recent , current month ) Covers applications of computer science to the mathematical modeling of complex systems in the fields of science, engineering, and finance. Papers here are interdisciplinary and applications-oriented, focusing on techniques and tools that enable challenging computational simulations to be performed, for which the use of supercomputers or distributed computing platforms is often required. Includes material in ACM Subject Classes J.2, J.3, and J.4 (economics).
cs.CG - Computational Geometry ( new , recent , current month ) Roughly includes material in ACM Subject Classes I.3.5 and F.2.2.
cs.GT - Computer Science and Game Theory ( new , recent , current month ) Covers all theoretical and applied aspects at the intersection of computer science and game theory, including work in mechanism design, learning in games (which may overlap with Learning), foundations of agent modeling in games (which may overlap with Multiagent systems), coordination, specification and formal methods for non-cooperative computational environments. The area also deals with applications of game theory to areas such as electronic commerce.
cs.CV - Computer Vision and Pattern Recognition ( new , recent , current month ) Covers image processing, computer vision, pattern recognition, and scene understanding. Roughly includes material in ACM Subject Classes I.2.10, I.4, and I.5.
cs.CY - Computers and Society ( new , recent , current month ) Covers impact of computers on society, computer ethics, information technology and public policy, legal aspects of computing, computers and education. Roughly includes material in ACM Subject Classes K.0, K.2, K.3, K.4, K.5, and K.7.
cs.CR - Cryptography and Security ( new , recent , current month ) Covers all areas of cryptography and security including authentication, public key cryptosytems, proof-carrying code, etc. Roughly includes material in ACM Subject Classes D.4.6 and E.3.
cs.DS - Data Structures and Algorithms ( new , recent , current month ) Covers data structures and analysis of algorithms. Roughly includes material in ACM Subject Classes E.1, E.2, F.2.1, and F.2.2.
cs.DB - Databases ( new , recent , current month ) Covers database management, datamining, and data processing. Roughly includes material in ACM Subject Classes E.2, E.5, H.0, H.2, and J.1.
cs.DL - Digital Libraries ( new , recent , current month ) Covers all aspects of the digital library design and document and text creation. Note that there will be some overlap with Information Retrieval (which is a separate subject area). Roughly includes material in ACM Subject Classes H.3.5, H.3.6, H.3.7, I.7.
cs.DM - Discrete Mathematics ( new , recent , current month ) Covers combinatorics, graph theory, applications of probability. Roughly includes material in ACM Subject Classes G.2 and G.3.
cs.DC - Distributed, Parallel, and Cluster Computing ( new , recent , current month ) Covers fault-tolerance, distributed algorithms, stabilility, parallel computation, and cluster computing. Roughly includes material in ACM Subject Classes C.1.2, C.1.4, C.2.4, D.1.3, D.4.5, D.4.7, E.1.
cs.ET - Emerging Technologies ( new , recent , current month ) Covers approaches to information processing (computing, communication, sensing) and bio-chemical analysis based on alternatives to silicon CMOS-based technologies, such as nanoscale electronic, photonic, spin-based, superconducting, mechanical, bio-chemical and quantum technologies (this list is not exclusive). Topics of interest include (1) building blocks for emerging technologies, their scalability and adoption in larger systems, including integration with traditional technologies, (2) modeling, design and optimization of novel devices and systems, (3) models of computation, algorithm design and programming for emerging technologies.
cs.FL - Formal Languages and Automata Theory ( new , recent , current month ) Covers automata theory, formal language theory, grammars, and combinatorics on words. This roughly corresponds to ACM Subject Classes F.1.1, and F.4.3. Papers dealing with computational complexity should go to cs.CC; papers dealing with logic should go to cs.LO.
cs.GL - General Literature ( new , recent , current month ) Covers introductory material, survey material, predictions of future trends, biographies, and miscellaneous computer-science related material. Roughly includes all of ACM Subject Class A, except it does not include conference proceedings (which will be listed in the appropriate subject area).
cs.GR - Graphics ( new , recent , current month ) Covers all aspects of computer graphics. Roughly includes material in all of ACM Subject Class I.3, except that I.3.5 is is likely to have Computational Geometry as the primary subject area.
cs.AR - Hardware Architecture ( new , recent , current month ) Covers systems organization and hardware architecture. Roughly includes material in ACM Subject Classes C.0, C.1, and C.5.
cs.HC - Human-Computer Interaction ( new , recent , current month ) Covers human factors, user interfaces, and collaborative computing. Roughly includes material in ACM Subject Classes H.1.2 and all of H.5, except for H.5.1, which is more likely to have Multimedia as the primary subject area.
cs.IR - Information Retrieval ( new , recent , current month ) Covers indexing, dictionaries, retrieval, content and analysis. Roughly includes material in ACM Subject Classes H.3.0, H.3.1, H.3.2, H.3.3, and H.3.4.
cs.IT - Information Theory ( new , recent , current month ) Covers theoretical and experimental aspects of information theory and coding. Includes material in ACM Subject Class E.4 and intersects with H.1.1.
cs.LO - Logic in Computer Science ( new , recent , current month ) Covers all aspects of logic in computer science, including finite model theory, logics of programs, modal logic, and program verification. Programming language semantics should have Programming Languages as the primary subject area. Roughly includes material in ACM Subject Classes D.2.4, F.3.1, F.4.0, F.4.1, and F.4.2; some material in F.4.3 (formal languages) may also be appropriate here, although Computational Complexity is typically the more appropriate subject area.
cs.LG - Machine Learning ( new , recent , current month ) Papers on all aspects of machine learning research (supervised, unsupervised, reinforcement learning, bandit problems, and so on) including also robustness, explanation, fairness, and methodology. cs.LG is also an appropriate primary category for applications of machine learning methods.
cs.MS - Mathematical Software ( new , recent , current month ) Roughly includes material in ACM Subject Class G.4.
cs.MA - Multiagent Systems ( new , recent , current month ) Covers multiagent systems, distributed artificial intelligence, intelligent agents, coordinated interactions. and practical applications. Roughly covers ACM Subject Class I.2.11.
cs.MM - Multimedia ( new , recent , current month ) Roughly includes material in ACM Subject Class H.5.1.
cs.NI - Networking and Internet Architecture ( new , recent , current month ) Covers all aspects of computer communication networks, including network architecture and design, network protocols, and internetwork standards (like TCP/IP). Also includes topics, such as web caching, that are directly relevant to Internet architecture and performance. Roughly includes all of ACM Subject Class C.2 except C.2.4, which is more likely to have Distributed, Parallel, and Cluster Computing as the primary subject area.
cs.NE - Neural and Evolutionary Computing ( new , recent , current month ) Covers neural networks, connectionism, genetic algorithms, artificial life, adaptive behavior. Roughly includes some material in ACM Subject Class C.1.3, I.2.6, I.5.
cs.NA - Numerical Analysis ( new , recent , current month ) cs.NA is an alias for math.NA. Roughly includes material in ACM Subject Class G.1.
cs.OS - Operating Systems ( new , recent , current month ) Roughly includes material in ACM Subject Classes D.4.1, D.4.2., D.4.3, D.4.4, D.4.5, D.4.7, and D.4.9.
cs.OH - Other Computer Science ( new , recent , current month ) This is the classification to use for documents that do not fit anywhere else.
cs.PF - Performance ( new , recent , current month ) Covers performance measurement and evaluation, queueing, and simulation. Roughly includes material in ACM Subject Classes D.4.8 and K.6.2.
cs.PL - Programming Languages ( new , recent , current month ) Covers programming language semantics, language features, programming approaches (such as object-oriented programming, functional programming, logic programming). Also includes material on compilers oriented towards programming languages; other material on compilers may be more appropriate in Architecture (AR). Roughly includes material in ACM Subject Classes D.1 and D.3.
cs.RO - Robotics ( new , recent , current month ) Roughly includes material in ACM Subject Class I.2.9.
cs.SI - Social and Information Networks ( new , recent , current month ) Covers the design, analysis, and modeling of social and information networks, including their applications for on-line information access, communication, and interaction, and their roles as datasets in the exploration of questions in these and other domains, including connections to the social and biological sciences. Analysis and modeling of such networks includes topics in ACM Subject classes F.2, G.2, G.3, H.2, and I.2; applications in computing include topics in H.3, H.4, and H.5; and applications at the interface of computing and other disciplines include topics in J.1--J.7. Papers on computer communication systems and network protocols (e.g. TCP/IP) are generally a closer fit to the Networking and Internet Architecture (cs.NI) category.
cs.SE - Software Engineering ( new , recent , current month ) Covers design tools, software metrics, testing and debugging, programming environments, etc. Roughly includes material in all of ACM Subject Classes D.2, except that D.2.4 (program verification) should probably have Logics in Computer Science as the primary subject area.
cs.SD - Sound ( new , recent , current month ) Covers all aspects of computing with sound, and sound as an information channel. Includes models of sound, analysis and synthesis, audio user interfaces, sonification of data, computer music, and sound signal processing. Includes ACM Subject Class H.5.5, and intersects with H.1.2, H.5.1, H.5.2, I.2.7, I.5.4, I.6.3, J.5, K.4.2.
cs.SC - Symbolic Computation ( new , recent , current month ) Roughly includes material in ACM Subject Class I.1.
cs.SY - Systems and Control ( new , recent , current month ) cs.SY is an alias for eess.SY. This section includes theoretical and experimental research covering all facets of automatic control systems. The section is focused on methods of control system analysis and design using tools of modeling, simulation and optimization. Specific areas of research include nonlinear, distributed, adaptive, stochastic and robust control in addition to hybrid and discrete event systems. Application areas include automotive and aerospace control systems, network control, biological systems, multiagent and cooperative control, robotics, reinforcement learning, sensor networks, control of cyber-physical and energy-related systems, and control of computing systems.

Advisers & Contacts
Bachelor of Arts & Bachelor of Science in Engineering
Prerequisites
Declaring Computer Science for AB Students
Declaring Computer Science for BSE Students
Class of '25, '26 & '27 - Departmental Requirements
Class of 2024 - Departmental Requirements
COS126 Information
Important Steps and Deadlines
Independent Work Seminars
Guidelines and Useful Information

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Independent Work & Theses

How to Contact Faculty for IW/Thesis Advising

Send the professor an e-mail. When you write a professor, be clear that you want a meeting regarding a senior thesis or one-on-one IW project, and briefly describe the topic or idea that you want to work on. Check the faculty listing for email addresses.

Parastoo Abtahi, Room 419

Available for single-semester IW and senior thesis advising, 2023-2024

Research Areas: Human-Computer Interaction (HCI), Augmented Reality (AR), and Spatial Computing
Input techniques for on-the-go interaction (e.g., eye-gaze, microgestures, voice) with a focus on uncertainty, disambiguation, and privacy.
Minimal and timely multisensory output (e.g., spatial audio, haptics) that enables users to attend to their physical environment and the people around them, instead of a 2D screen.
Interaction with intelligent systems (e.g., IoT, robots) situated in physical spaces with a focus on updating users’ mental model despite the complexity and dynamicity of these systems.

Ryan Adams, Room 411

Research areas:

Machine learning driven design
Generative models for structured discrete objects
Approximate inference in probabilistic models
Accelerating solutions to partial differential equations
Innovative uses of automatic differentiation
Modeling and optimizing 3d printing and CNC machining

Andrew Appel, Room 209

Research Areas: Formal methods, programming languages, compilers, computer security.
Software verification (for which taking COS 326 / COS 510 is helpful preparation)
Game theory of poker or other games (for which COS 217 / 226 are helpful)
Computer game-playing programs (for which COS 217 / 226)
Risk-limiting audits of elections (for which ORF 245 or other knowledge of probability is useful)

Sanjeev Arora, Room 407

Theoretical machine learning, deep learning and its analysis, natural language processing. My advisees would typically have taken a course in algorithms (COS423 or COS 521 or equivalent) and a course in machine learning.
Show that finding approximate solutions to NP-complete problems is also NP-complete (i.e., come up with NP-completeness reductions a la COS 487).
Experimental Algorithms: Implementing and Evaluating Algorithms using existing software packages.
Studying/designing provable algorithms for machine learning and implementions using packages like scipy and MATLAB, including applications in Natural language processing and deep learning.
Any topic in theoretical computer science.

David August, Room 221

Research Areas: Computer Architecture, Compilers, Parallelism
Containment-based approaches to security: We have designed and tested a simple hardware+software containment mechanism that stops incorrect communication resulting from faults, bugs, or exploits from leaving the system. Let's explore ways to use containment to solve real problems. Expect to work with corporate security and technology decision-makers.
Parallelism: Studies show much more parallelism than is currently realized in compilers and architectures. Let's find ways to realize this parallelism.
Any other interesting topic in computer architecture or compilers.

Mark Braverman, 194 Nassau St., Room 231

Available for Spring 2024 single-semester IW, only

Research Areas: computational complexity, algorithms, applied probability, computability over the real numbers, game theory and mechanism design, information theory.
Topics in computational and communication complexity.
Applications of information theory in complexity theory.
Algorithms for problems under real-life assumptions.
Game theory, network effects
Mechanism design (could be on a problem proposed by the student)

Sebastian Caldas, 221 Nassau Street, Room 105

Research Areas: collaborative learning, machine learning for healthcare. Typically, I will work with students that have taken COS324.
Methods for collaborative and continual learning.
Machine learning for healthcare applications.

Bernard Chazelle, 194 Nassau St., Room 301

Research Areas: Natural Algorithms, Computational Geometry, Sublinear Algorithms.
Natural algorithms (flocking, swarming, social networks, etc).
Sublinear algorithms
Self-improving algorithms
Markov data structures

Danqi Chen, Room 412

Not available for IW or thesis advising, 2023-2024

My advisees would be expected to have taken a course in machine learning and ideally have taken COS484 or an NLP graduate seminar.
Representation learning for text and knowledge bases
Pre-training and transfer learning
Question answering and reading comprehension
Information extraction
Text summarization
Any other interesting topics related to natural language understanding/generation

Marcel Dall'Agnol, Corwin 034

Available for single-semester and senior thesis advising, 2023-2024

Research Areas: Theoretical computer science. (Specifically, quantum computation, sublinear algorithms, complexity theory, interactive proofs and cryptography)

Jia Deng, Room 423

Available for Fall 2023 single-semester IW, only

Research Areas: Computer Vision, Machine Learning.
Object recognition and action recognition
Deep Learning, autoML, meta-learning
Geometric reasoning, logical reasoning

Adji Bousso Dieng, Room 406

Research areas: Vertaix is a research lab at Princeton University led by Professor Adji Bousso Dieng. We work at the intersection of artificial intelligence (AI) and the natural sciences. The models and algorithms we develop are motivated by problems in those domains and contribute to advancing methodological research in AI. We leverage tools in statistical machine learning and deep learning in developing methods for learning with the data, of various modalities, arising from the natural sciences.

Robert Dondero, Corwin Hall, Room 038

Research Areas: Software engineering; software engineering education.
Develop or evaluate tools to facilitate student learning in undergraduate computer science courses at Princeton, and beyond.
In particular, can code critiquing tools help students learn about software quality?

Zeev Dvir, 194 Nassau St., Room 250

Not available for IW or thesis advising, 2023-2024.

Research Areas: computational complexity, pseudo-randomness, coding theory and discrete mathematics.
Independent Research: I have various research problems related to Pseudorandomness, Coding theory, Complexity and Discrete mathematics - all of which require strong mathematical background. A project could also be based on writing a survey paper describing results from a few theory papers revolving around some particular subject.

Benjamin Eysenbach, Room 416

Research areas: reinforcement learning, machine learning. My advisees would typically have taken COS324.
Using RL algorithms to applications in science and engineering.
Emergent behavior of RL algorithms on high-fidelity robotic simulators.
Studying how architectures and representations can facilitate generalization.

Christiane Fellbaum, 1-S-14 Green

No longer available for single-term IW and senior thesis advising, 2023-2024

Research Areas: theoretical and computational linguistics, word sense disambiguation, lexical resource construction, English and multilingual WordNet(s), ontology
Anything having to do with natural language--come and see me with/for ideas suitable to your background and interests. Some topics students have worked on in the past:
Developing parsers, part-of-speech taggers, morphological analyzers for underrepresented languages (you don't have to know the language to develop such tools!)
Quantitative approaches to theoretical linguistics questions
Extensions and interfaces for WordNet (English and WN in other languages),
Applications of WordNet(s), including:
Foreign language tutoring systems,
Spelling correction software,
Word-finding/suggestion software for ordinary users and people with memory problems,
Machine Translation
Sentiment and Opinion detection
Automatic reasoning and inferencing
Collaboration with professors in the social sciences and humanities ("Digital Humanities")

Adam Finkelstein, Room 424

Research Areas: computer graphics, audio.

Robert S. Fish, Corwin Hall, Room 037

No longer available for single-semester IW and senior thesis advising, 2023-2024

Networking and telecommunications
Learning, perception, and intelligence, artificial and otherwise;
Human-computer interaction and computer-supported cooperative work
Online education, especially in Computer Science Education
Topics in research and development innovation methodologies including standards, open-source, and entrepreneurship
Distributed autonomous organizations and related blockchain technologies

Michael Freedman, Room 308

Research Areas: Distributed systems, security, networking
Projects related to streaming data analysis, datacenter systems and networks, untrusted cloud storage and applications. Please see my group website at http://sns.cs.princeton.edu/ for current research projects.

Ruth Fong, Room 032

Research Areas: computer vision, machine learning, deep learning, interpretability, explainable AI, fairness and bias in AI
Develop a technique for understanding AI models
Design a AI model that is interpretable by design
Build a paradigm for detecting and/or correcting failure points in an AI model
Analyze an existing AI model and/or dataset to better understand its failure points
Build a computer vision system for another domain (e.g., medical imaging, satellite data, etc.)
Develop a software package for explainable AI
Adapt explainable AI research to a consumer-facing problem

Note: I am happy to advise any project if there's a sufficient overlap in interest and/or expertise; please reach out via email to chat about project ideas.

Tom Griffiths, Room 405

Research areas: computational cognitive science, computational social science, machine learning and artificial intelligence

Note: I am open to projects that apply ideas from computer science to understanding aspects of human cognition in a wide range of areas, from decision-making to cultural evolution and everything in between. For example, we have current projects analyzing chess game data and magic tricks, both of which give us clues about how human minds work. Students who have expertise or access to data related to games, magic, strategic sports like fencing, or other quantifiable domains of human behavior feel free to get in touch.

Aarti Gupta, Room 220

Research Areas: Formal methods, program analysis, logic decision procedures
Finding bugs in open source software using automatic verification tools
Software verification (program analysis, model checking, test generation)
Decision procedures for logical reasoning (SAT solvers, SMT solvers)

Elad Hazan, Room 409

Research interests: machine learning methods and algorithms, efficient methods for mathematical optimization, regret minimization in games, reinforcement learning, control theory and practice
Machine learning, efficient methods for mathematical optimization, statistical and computational learning theory, regret minimization in games.
Implementation and algorithm engineering for control, reinforcement learning and robotics
Implementation and algorithm engineering for time series prediction

Felix Heide, Room 410

Research Areas: Computational Imaging, Computer Vision, Machine Learning (focus on Optimization and Approximate Inference).
Optical Neural Networks
Hardware-in-the-loop Holography
Zero-shot and Simulation-only Learning
Object recognition in extreme conditions
3D Scene Representations for View Generation and Inverse Problems
Long-range Imaging in Scattering Media
Hardware-in-the-loop Illumination and Sensor Optimization
Inverse Lidar Design
Phase Retrieval Algorithms
Proximal Algorithms for Learning and Inference
Domain-Specific Language for Optics Design

Kyle Jamieson, Room 306

Research areas: Wireless and mobile networking; indoor radar and indoor localization; Internet of Things
See other topics on my independent work ideas page (campus IP and CS dept. login req'd)

Alan Kaplan, 221 Nassau Street, Room 105

Research Areas:

Random apps of kindness - mobile application/technology frameworks used to help individuals or communities; topic areas include, but are not limited to: first response, accessibility, environment, sustainability, social activism, civic computing, tele-health, remote learning, crowdsourcing, etc.
Tools automating programming language interoperability - Java/C++, React Native/Java, etc.
Software visualization tools for education
Connected consumer devices, applications and protocols

Brian Kernighan, Room 311

Research Areas: application-specific languages, document preparation, user interfaces, software tools, programming methodology
Application-oriented languages, scripting languages.
Tools; user interfaces
Digital humanities

Zachary Kincaid, Room 219

Research areas: programming languages, program analysis, program verification, automated reasoning
Independent Research Topics:
Develop a practical algorithm for an intractable problem (e.g., by developing practical search heuristics, or by reducing to, or by identifying a tractable sub-problem, ...).
Design a domain-specific programming language, or prototype a new feature for an existing language.
Any interesting project related to programming languages or logic.

Gillat Kol, Room 316

Aleksandra korolova, 309 sherrerd hall.

Available for single-term IW and senior thesis advising, 2023-2024

Research areas: Societal impacts of algorithms and AI; privacy; fair and privacy-preserving machine learning; algorithm auditing.

Advisees typically have taken one or more of COS 226, COS 324, COS 423, COS 424 or COS 445.

Amit Levy, Room 307

Research Areas: Operating Systems, Distributed Systems, Embedded Systems, Internet of Things
Distributed hardware testing infrastructure
Second factor security tokens
Low-power wireless network protocol implementation
USB device driver implementation

Kai Li, Room 321

Research Areas: Distributed systems; storage systems; content-based search and data analysis of large datasets.
Fast communication mechanisms for heterogeneous clusters.
Approximate nearest-neighbor search for high dimensional data.
Data analysis and prediction of in-patient medical data.
Optimized implementation of classification algorithms on manycore processors.

Xiaoyan Li, 221 Nassau Street, Room 104

Research areas: Information retrieval, novelty detection, question answering, AI, machine learning and data analysis.
Explore new statistical retrieval models for document retrieval and question answering.
Apply AI in various fields.
Apply supervised or unsupervised learning in health, education, finance, and social networks, etc.
Any interesting project related to AI, machine learning, and data analysis.

Wyatt Lloyd, Room 323

Research areas: Distributed Systems
Caching algorithms and implementations
Storage systems
Distributed transaction algorithms and implementations

Margaret Martonosi, Room 208

Quantum Computing research, particularly related to architecture and compiler issues for QC.
Computer architectures specialized for modern workloads (e.g., graph analytics, machine learning algorithms, mobile applications
Investigating security and privacy vulnerabilities in computer systems, particularly IoT devices.
Other topics in computer architecture or mobile / IoT systems also possible.

Jonathan Mayer, Sherrerd Hall, Room 307

Research areas: Technology law and policy, with emphasis on national security, criminal procedure, consumer privacy, network management, and online speech.
Assessing the effects of government policies, both in the public and private sectors.
Collecting new data that relates to government decision making, including surveying current business practices and studying user behavior.
Developing new tools to improve government processes and offer policy alternatives.

Andrés Monroy-Hernández, Room 405

Research Areas: Human-Computer Interaction, Social Computing, Public-Interest Technology, Augmented Reality, Urban Computing
Research interests:developing public-interest socio-technical systems. We are currently creating alternatives to gig work platforms that are more equitable for all stakeholders. For instance, we are investigating the socio-technical affordances necessary to support a co-op food delivery network owned and managed by workers and restaurants. We are exploring novel system designs that support self-governance, decentralized/federated models, community-centered data ownership, and portable reputation systems. We have opportunities for students interested in human-centered computing, UI/UX design, full-stack software development, and qualitative/quantitative user research.
Beyond our core projects, we are open to working on research projects that explore the use of emerging technologies, such as AR, wearables, NFTs, and DAOs, for creative and out-of-the-box applications.

Christopher Moretti, Corwin Hall, Room 036

Research areas: Distributed systems, high-throughput computing, computer science/engineering education
Expansion, improvement, and evaluation of open-source distributed computing software.
Applications of distributed computing for "big science" (e.g. biometrics, data mining, bioinformatics)
Software and best practices for computer science education and study, especially Princeton's 126/217/226 sequence or MOOCs development
Sports analytics and/or crowd-sourced computing

Radhika Nagpal, F316 Engineering Quadrangle

Research areas: control, robotics and dynamical systems

Karthik Narasimhan, Room 422

Research areas: Natural Language Processing, Reinforcement Learning
Autonomous agents for text-based games ( https://www.microsoft.com/en-us/research/project/textworld/ )
Transfer learning/generalization in NLP
Techniques for generating natural language
Model-based reinforcement learning

Arvind Narayanan, 308 Sherrerd Hall

Research Areas: fair machine learning (and AI ethics more broadly), the social impact of algorithmic systems, tech policy

Pedro Paredes, Corwin Hall, Room 041

My primary research work is in Theoretical Computer Science.

* Research Interest: Spectral Graph theory, Pseudorandomness, Complexity theory, Coding Theory, Quantum Information Theory, Combinatorics.

The IW projects I am interested in advising can be divided into three categories:

1. Theoretical research

I am open to advise work on research projects in any topic in one of my research areas of interest. A project could also be based on writing a survey given results from a few papers. Students should have a solid background in math (e.g., elementary combinatorics, graph theory, discrete probability, basic algebra/calculus) and theoretical computer science (226 and 240 material, like big-O/Omega/Theta, basic complexity theory, basic fundamental algorithms). Mathematical maturity is a must.

A (non exhaustive) list of topics of projects I'm interested in: * Explicit constructions of better vertex expanders and/or unique neighbor expanders. * Construction deterministic or random high dimensional expanders. * Pseudorandom generators for different problems. * Topics around the quantum PCP conjecture. * Topics around quantum error correcting codes and locally testable codes, including constructions, encoding and decoding algorithms.

2. Theory informed practical implementations of algorithms Very often the great advances in theoretical research are either not tested in practice or not even feasible to be implemented in practice. Thus, I am interested in any project that consists in trying to make theoretical ideas applicable in practice. This includes coming up with new algorithms that trade some theoretical guarantees for feasible implementation yet trying to retain the soul of the original idea; implementing new algorithms in a suitable programming language; and empirically testing practical implementations and comparing them with benchmarks / theoretical expectations. A project in this area doesn't have to be in my main areas of research, any theoretical result could be suitable for such a project.

Some examples of areas of interest: * Streaming algorithms. * Numeric linear algebra. * Property testing. * Parallel / Distributed algorithms. * Online algorithms. 3. Machine learning with a theoretical foundation

I am interested in projects in machine learning that have some mathematical/theoretical, even if most of the project is applied. This includes topics like mathematical optimization, statistical learning, fairness and privacy.

One particular area I have been recently interested in is in the area of rating systems (e.g., Chess elo) and applications of this to experts problems.

Final Note: I am also willing to advise any project with any mathematical/theoretical component, even if it's not the main one; please reach out via email to chat about project ideas.

Iasonas Petras, Corwin Hall, Room 033

Research Areas: Information Based Complexity, Numerical Analysis, Quantum Computation.
Prerequisites: Reasonable mathematical maturity. In case of a project related to Quantum Computation a certain familiarity with quantum mechanics is required (related courses: ELE 396/PHY 208).
Possible research topics include:

1. Quantum algorithms and circuits:

i. Design or simulation quantum circuits implementing quantum algorithms.
ii. Design of quantum algorithms solving/approximating continuous problems (such as Eigenvalue problems for Partial Differential Equations).

2. Information Based Complexity:

i. Necessary and sufficient conditions for tractability of Linear and Linear Tensor Product Problems in various settings (for example worst case or average case).
ii. Necessary and sufficient conditions for tractability of Linear and Linear Tensor Product Problems under new tractability and error criteria.
iii. Necessary and sufficient conditions for tractability of Weighted problems.
iv. Necessary and sufficient conditions for tractability of Weighted Problems under new tractability and error criteria.

3. Topics in Scientific Computation:

i. Randomness, Pseudorandomness, MC and QMC methods and their applications (Finance, etc)

Yuri Pritykin, 245 Carl Icahn Lab

Research interests: Computational biology; Cancer immunology; Regulation of gene expression; Functional genomics; Single-cell technologies.
Potential research projects: Development, implementation, assessment and/or application of algorithms for analysis, integration, interpretation and visualization of multi-dimensional data in molecular biology, particularly single-cell and spatial genomics data.

Benjamin Raphael, Room 309

Research interests: Computational biology and bioinformatics; Cancer genomics; Algorithms and machine learning approaches for analysis of large-scale datasets
Implementation and application of algorithms to infer evolutionary processes in cancer
Identifying correlations between combinations of genomic mutations in human and cancer genomes
Design and implementation of algorithms for genome sequencing from new DNA sequencing technologies
Graph clustering and network anomaly detection, particularly using diffusion processes and methods from spectral graph theory

Vikram Ramaswamy, 035 Corwin Hall

Research areas: Interpretability of AI systems, Fairness in AI systems, Computer vision.
Constructing a new method to explain a model / create an interpretable by design model
Analyzing a current model / dataset to understand bias within the model/dataset
Proposing new fairness evaluations
Proposing new methods to train to improve fairness
Developing synthetic datasets for fairness / interpretability benchmarks
Understanding robustness of models

Ran Raz, Room 240

Research Area: Computational Complexity
Independent Research Topics: Computational Complexity, Information Theory, Quantum Computation, Theoretical Computer Science

Szymon Rusinkiewicz, Room 406

Research Areas: computer graphics; computer vision; 3D scanning; 3D printing; robotics; documentation and visualization of cultural heritage artifacts
Research ways of incorporating rotation invariance into computer visiontasks such as feature matching and classification
Investigate approaches to robust 3D scan matching
Model and compensate for imperfections in 3D printing
Given a collection of small mobile robots, apply control policies learned in simulation to the real robots.

Olga Russakovsky, Room 408

Research Areas: computer vision, machine learning, deep learning, crowdsourcing, fairness&bias in AI
Design a semantic segmentation deep learning model that can operate in a zero-shot setting (i.e., recognize and segment objects not seen during training)
Develop a deep learning classifier that is impervious to protected attributes (such as gender or race) that may be erroneously correlated with target classes
Build a computer vision system for the novel task of inferring what object (or part of an object) a human is referring to when pointing to a single pixel in the image. This includes both collecting an appropriate dataset using crowdsourcing on Amazon Mechanical Turk, creating a new deep learning formulation for this task, and running extensive analysis of both the data and the model

Sebastian Seung, Princeton Neuroscience Institute, Room 153

Research Areas: computational neuroscience, connectomics, "deep learning" neural networks, social computing, crowdsourcing, citizen science
Gamification of neuroscience (EyeWire 2.0)
Semantic segmentation and object detection in brain images from microscopy
Computational analysis of brain structure and function
Neural network theories of brain function

Jaswinder Pal Singh, Room 324

Research Areas: Boundary of technology and business/applications; building and scaling technology companies with special focus at that boundary; parallel computing systems and applications: parallel and distributed applications and their implications for software and architectural design; system software and programming environments for multiprocessors.
Develop a startup company idea, and build a plan/prototype for it.
Explore tradeoffs at the boundary of technology/product and business/applications in a chosen area.
Study and develop methods to infer insights from data in different application areas, from science to search to finance to others.
Design and implement a parallel application. Possible areas include graphics, compression, biology, among many others. Analyze performance bottlenecks using existing tools, and compare programming models/languages.
Design and implement a scalable distributed algorithm.

Mona Singh, Room 420

Research Areas: computational molecular biology, as well as its interface with machine learning and algorithms.
Whole and cross-genome methods for predicting protein function and protein-protein interactions.
Analysis and prediction of biological networks.
Computational methods for inferring specific aspects of protein structure from protein sequence data.
Any other interesting project in computational molecular biology.

Robert Tarjan, 194 Nassau St., Room 308

Available for single-semester IW and senior thesis advising, 2022-2023

Research Areas: Data structures; graph algorithms; combinatorial optimization; computational complexity; computational geometry; parallel algorithms.
Implement one or more data structures or combinatorial algorithms to provide insight into their empirical behavior.
Design and/or analyze various data structures and combinatorial algorithms.

Olga Troyanskaya, Room 320

Research Areas: Bioinformatics; analysis of large-scale biological data sets (genomics, gene expression, proteomics, biological networks); algorithms for integration of data from multiple data sources; visualization of biological data; machine learning methods in bioinformatics.
Implement and evaluate one or more gene expression analysis algorithm.
Develop algorithms for assessment of performance of genomic analysis methods.
Develop, implement, and evaluate visualization tools for heterogeneous biological data.

David Walker, Room 211

Research Areas: Programming languages, type systems, compilers, domain-specific languages, software-defined networking and security
Independent Research Topics: Any other interesting project that involves humanitarian hacking, functional programming, domain-specific programming languages, type systems, compilers, software-defined networking, fault tolerance, language-based security, theorem proving, logic or logical frameworks.

Shengyi Wang, Postdoctoral Research Associate, Room 216

Independent Research topics: Explore Escher-style tilings using (introductory) group theory and automata theory to produce beautiful pictures.

Kevin Wayne, Corwin Hall, Room 040

Research Areas: design, analysis, and implementation of algorithms; data structures; combinatorial optimization; graphs and networks.
Design and implement computer visualizations of algorithms or data structures.
Develop pedagogical tools or programming assignments for the computer science curriculum at Princeton and beyond.
Develop assessment infrastructure and assessments for MOOCs.

Matt Weinberg, 194 Nassau St., Room 222

Research Areas: algorithms, algorithmic game theory, mechanism design, game theoretical problems in {Bitcoin, networking, healthcare}.
Theoretical questions related to COS 445 topics such as matching theory, voting theory, auction design, etc.
Theoretical questions related to incentives in applications like Bitcoin, the Internet, health care, etc. In a little bit more detail: protocols for these systems are often designed assuming that users will follow them. But often, users will actually be strictly happier to deviate from the intended protocol. How should we reason about user behavior in these protocols? How should we design protocols in these settings?

Huacheng Yu, Room 310

data structures
streaming algorithms
design and analyze data structures / streaming algorithms
prove impossibility results (lower bounds)
implement and evaluate data structures / streaming algorithms

Ellen Zhong, Room 314

No longer available for single-term IW and senior thesis advising, 2023-2024

Opportunities outside the department

We encourage students to look in to doing interdisciplinary computer science research and to work with professors in departments other than computer science. However, every CS independent work project must have a strong computer science element (even if it has other scientific or artistic elements as well.) To do a project with an adviser outside of computer science you must have permission of the department. This can be accomplished by having a second co-adviser within the computer science department or by contacting the independent work supervisor about the project and having he or she sign the independent work proposal form.

Here is a list of professors outside the computer science department who are eager to work with computer science undergraduates.

Maria Apostolaki, Engineering Quadrangle, C330

Research areas: Computing & Networking, Data & Information Science, Security & Privacy

Branko Glisic, Engineering Quadrangle, Room E330

Documentation of historic structures
Cyber physical systems for structural health monitoring
Developing virtual and augmented reality applications for documenting structures
Applying machine learning techniques to generate 3D models from 2D plans of buildings
Contact : Rebecca Napolitano, rkn2 (@princeton.edu)

Mihir Kshirsagar, Sherrerd Hall, Room 315

Center for Information Technology Policy.

Consumer protection
Content regulation
Competition law
Economic development
Surveillance and discrimination

Sharad Malik, Engineering Quadrangle, Room B224

Select a Senior Thesis Adviser for the 2020-21 Academic Year.

Design of reliable hardware systems
Verifying complex software and hardware systems

Prateek Mittal, Engineering Quadrangle, Room B236

Internet security and privacy
Social Networks
Privacy technologies, anonymous communication
Network Science
Internet security and privacy: The insecurity of Internet protocols and services threatens the safety of our critical network infrastructure and billions of end users. How can we defend end users as well as our critical network infrastructure from attacks?
Trustworthy social systems: Online social networks (OSNs) such as Facebook, Google+, and Twitter have revolutionized the way our society communicates. How can we leverage social connections between users to design the next generation of communication systems?
Privacy Technologies: Privacy on the Internet is eroding rapidly, with businesses and governments mining sensitive user information. How can we protect the privacy of our online communications? The Tor project (https://www.torproject.org/) is a potential application of interest.

Ken Norman, Psychology Dept, PNI 137

Research Areas: Memory, the brain and computation
Lab: Princeton Computational Memory Lab

Potential research topics

Methods for decoding cognitive state information from neuroimaging data (fMRI and EEG)
Neural network simulations of learning and memory

Caroline Savage

Office of Sustainability, Phone:(609)258-7513, Email: cs35 (@princeton.edu)

The Campus as Lab program supports students using the Princeton campus as a living laboratory to solve sustainability challenges. The Office of Sustainability has created a list of campus as lab research questions, filterable by discipline and topic, on its website .

An example from Computer Science could include using TigerEnergy , a platform which provides real-time data on campus energy generation and consumption, to study one of the many energy systems or buildings on campus. Three CS students used TigerEnergy to create a live energy heatmap of campus .

Other potential projects include:

Apply game theory to sustainability challenges
Develop a tool to help visualize interactions between complex campus systems, e.g. energy and water use, transportation and storm water runoff, purchasing and waste, etc.
How can we learn (in aggregate) about individuals’ waste, energy, transportation, and other behaviors without impinging on privacy?

Janet Vertesi, Sociology Dept, Wallace Hall, Room 122

Research areas: Sociology of technology; Human-computer interaction; Ubiquitous computing.
Possible projects: At the intersection of computer science and social science, my students have built mixed reality games, produced artistic and interactive installations, and studied mixed human-robot teams, among other projects.

David Wentzlaff, Engineering Quadrangle, Room 228

Computing, Operating Systems, Sustainable Computing.

Instrument Princeton's Green (HPCRC) data center
Investigate power utilization on an processor core implemented in an FPGA
Dismantle and document all of the components in modern electronics. Invent new ways to build computers that can be recycled easier.
Other topics in parallel computer architecture or operating systems

Exploring Exciting Computer Science Research Topics: Unveiling the Frontiers

Are you searching for the best computer science research topics? If yes, then your search ends here with the best ever computer science research topics.

Computer science research is a dynamic and ever-evolving field that explores the vast possibilities of technology and its impact on society. With the rapid advancement of computing technologies, computer science researchers delve into a wide range of topics, seeking to solve complex problems, develop innovative solutions, and shape the future of technology. From artificial intelligence and data science to cybersecurity and human-computer interaction, the research landscape in computer science is vast and diverse.

In this guide, we will explore a variety of computer science research topics, shedding light on the exciting areas of study and the potential they hold. These research topics delve into the fundamental principles of computer science and extend their reach into specialized domains, aiming to make groundbreaking advancements, improve systems, and address real-world challenges.

Whether you are a student looking for an inspiring research topic, a researcher seeking to expand your horizons, or simply someone interested in the latest advancements in computer science, this guide will provide a glimpse into the breadth and depth of computer science research topics.

From exploring the frontiers of artificial intelligence and machine learning to examining the social implications of technology and the intersection of computer science with other disciplines, there is something for everyone in the world of computer science research.

By delving into these research topics, we uncover the potential for groundbreaking discoveries, technological advancements, and transformative solutions that have the power to shape the future. Computer science research is a driving force behind innovation and progress, and it offers endless possibilities for those who are curious, creative, and committed to pushing the boundaries of knowledge.

So, let us embark on this journey of exploration and discovery, as we delve into the fascinating realm of computer science research topics, where innovation and imagination converge to pave the way for a better and more technologically advanced future.

Significance of Computer Science Research

Table of Contents

Computer science research plays a crucial role in driving technological advancements, innovation, and societal progress. Here are some key aspects highlighting the significance of computer science research:

Advancing Technology

Computer science research leads to the development of new technologies, algorithms, and systems that improve various aspects of our lives. It fuels advancements in fields such as artificial intelligence, data science, cybersecurity, robotics, and more.

Solving Complex Problems

Computer science research tackles complex challenges and problems that require innovative solutions. Researchers work on developing algorithms, models, and methodologies to address issues related to healthcare, climate change, urban planning, transportation, and other critical domains.

Improving Efficiency and Productivity

Through research, computer scientists strive to optimize systems, algorithms, and processes, leading to increased efficiency and productivity in various industries. This includes streamlining operations, automating tasks, and enhancing decision-making processes.

Driving Economic Growth

Computer science research has a significant impact on the economy. It drives innovation, leads to the creation of new industries, and fosters entrepreneurial opportunities. Startups and technology companies emerge as a result of groundbreaking research, contributing to job creation and economic growth.

Enhancing Human-Computer Interaction

Research in human-computer interaction focuses on designing intuitive and user-friendly interfaces, improving accessibility, and exploring novel ways for humans to interact with computers and technology. This research leads to more seamless interactions and positive user experiences.

Addressing Societal Challenges

Computer science research plays a vital role in addressing societal challenges. It contributes to areas such as healthcare, education, environmental sustainability, social networks, and public safety. Researchers strive to develop solutions that have a positive impact on individuals and communities.

Shaping the Future

Computer science research is at the forefront of shaping the future. It explores emerging technologies like quantum computing, blockchain, augmented reality, and more. Through research, scientists anticipate and prepare for the technological advancements and challenges that lie ahead.

In summary, computer science research is of immense significance as it drives technological advancements, solves complex problems, improves efficiency, drives economic growth, enhances human-computer interaction, addresses societal challenges, and shapes the future. It is a crucial discipline that pushes the boundaries of innovation and fosters progress in various fields

Computer Science Research Topics

Have a close look at computer science research topics

Fundamental Research Topics

Fundamental research topics in computer science lay the groundwork for understanding and developing key principles and technologies. These areas serve as building blocks for numerous applications and advancements within the field. Here are some essential fundamental research topics:

Algorithms and Data Structures

Analysis and design of algorithms
Sorting and searching algorithms
Graph algorithms and network flow
Computational geometry
Data structures for efficient storage and retrieval

Artificial Intelligence and Machine Learning

Machine learning algorithms and models
Deep learning and neural networks
Natural language processing and understanding
Reinforcement learning and decision-making
Computer vision and pattern recognition

Computer Architecture and Systems

Processor and memory architecture
Parallel and distributed computing systems
Operating systems and resource management
High-performance computing
Embedded systems and Internet of Things (IoT)

Cryptography and Network Security

Encryption and decryption techniques
Cryptographic protocols and key management
Network security algorithms and protocols
Secure communication and authentication
Intrusion detection and prevention systems

Databases and Data Management

Relational and non-relational databases
Data modeling and database design
Query optimization and data indexing
Data mining and knowledge discovery
Big data storage and processing techniques

These fundamental research topics form the core of computer science, enabling advancements in various fields and applications. Researchers delve into these areas to improve efficiency, scalability, security, and intelligence in computer systems and software. By exploring these topics, researchers contribute to the foundation of computer science and pave the way for innovative technologies and solutions.

Emerging Research Topics

The field of computer science is dynamic, constantly evolving, and driven by emerging technologies and trends. Exploring emerging research topics allows researchers to stay at the forefront of innovation and address new challenges. Here are some prominent emerging research topics in computer science:

Quantum Computing

Quantum algorithms and computational models
Quantum error correction and fault tolerance
Quantum simulation and optimization
Quantum cryptography and secure communication
Applications of quantum computing in various domains

Internet of Things (IoT)

IoT architectures and protocols
IoT data analytics and machine learning
Energy-efficient IoT devices and networking
Security and privacy in IoT systems
IoT applications in smart cities, healthcare, and transportation

Big Data Analytics

Large-scale data processing and storage techniques
Data mining and machine learning on big data
Real-time analytics and stream processing
Big data visualization and exploratory analysis
Privacy-preserving techniques for big data analytics

Cybersecurity and Privacy

Threat detection and prevention mechanisms
Secure communication protocols and encryption
Privacy-preserving data sharing and analysis
Biometric authentication and identity management
Cybersecurity challenges in cloud computing and IoT

Human-Computer Interaction

User interface design and usability engineering
Augmented reality and virtual reality interfaces
User experience evaluation and user-centered design
Brain-computer interfaces and adaptive systems

Exploring these emerging research topics allows researchers to address current and future challenges in computer science. By investigating quantum computing, IoT, big data analytics, cybersecurity, and human-computer interaction, researchers contribute to the development of innovative solutions, algorithms, and systems that shape the future of technology. These areas offer immense potential for groundbreaking discoveries and transformative applications.

Interdisciplinary Research Topics

Computer science often intersects with other disciplines, leading to exciting interdisciplinary research opportunities. These interdisciplinary areas leverage computer science techniques and tools to address challenges in various domains. Here are some noteworthy interdisciplinary research topics in computer science:

Computational Biology and Bioinformatics

Genomic data analysis and sequencing algorithms
Protein structure prediction and molecular modeling
Computational drug discovery and personalized medicine
Systems biology and biological network analysis
Bioinformatics tools and databases

Computer Vision and Image Processing

Object detection and recognition
Image and video segmentation
Visual tracking and motion analysis
Deep learning for image understanding
Medical imaging and computer-aided diagnosis

Natural Language Processing and Text Mining

Sentiment analysis and opinion mining
Named entity recognition and entity linking
Question answering and dialogue systems
Text summarization and generation
Machine translation and language modeling

Robotics and Autonomous Systems

Robot perception and environment sensing
Motion planning and control algorithms
Human-robot interaction and collaboration
Swarm robotics and collective intelligence
Autonomous vehicles and drones

Social Network Analysis and Data Mining

Community detection and influence analysis
Information diffusion and rumor spreading
Recommender systems and personalized recommendations
Social media analytics and sentiment analysis
Behavioral modeling and social network privacy

These interdisciplinary research topics demonstrate the diverse applications and collaborative nature of computer science. By combining computer science principles with biology, image processing, linguistics, robotics, and social sciences, researchers can make significant contributions to multiple fields. These areas offer opportunities for groundbreaking discoveries, technological advancements, and real-world impact.

Research Topics in Specific Domains

Computer science research extends its reach into specific domains, where technological advancements can have a profound impact. By focusing on these specialized areas, researchers can address domain-specific challenges and contribute to advancements in various industries. Here are some research topics in specific domains:

Healthcare and Medical Informatics

Electronic health records and healthcare data analytics
Wearable devices and remote patient monitoring
Health informatics standards and interoperability
AI-based decision support systems in healthcare

Education Technology and E-Learning

Intelligent tutoring systems and personalized learning
Gamification and educational games
Adaptive e-learning platforms and learning analytics
Natural language processing for automated assessment
Virtual and augmented reality in education

Financial Technology (FinTech)

Blockchain technology and cryptocurrencies
Fraud detection and cybersecurity in financial systems
Robo-advisors and algorithmic trading
Digital payment systems and mobile banking
Risk assessment and predictive analytics in finance

Smart Cities and Urban Computing

Sensor networks and data-driven urban planning
Intelligent transportation systems and traffic management
Energy-efficient buildings and smart grid technologies
Urban sensing and environmental monitoring
Citizen engagement and participatory urban design

Gaming and Virtual Reality

Real-time rendering and graphics in video games
Physics simulation and collision detection in games
Virtual reality (VR) and augmented reality (AR) experiences
AI-driven game characters and procedural content generation
Multiplayer online gaming and network optimization

Researching these specific domains allows researchers to address industry-specific challenges and contribute to advancements in healthcare, education, finance, urban planning, and entertainment. By leveraging computer science principles and technologies, researchers can shape the future of these domains and create innovative solutions that have a tangible impact on society.

Choosing a Research Topic

Selecting the right research topic is crucial for a successful and fulfilling research journey in computer science. Several factors should be considered when choosing a research topic, ensuring its feasibility, relevance, and personal interest. Here are some key points to consider:

Factors to Consider when Selecting a Research Topic

Relevance and impact.

Choose a topic that addresses current challenges and has the potential for real-world impact.

Feasibility and Resources

Assess the availability of resources, data, and tools required to conduct research on the chosen topic.

Novelty and Contribution

Seek topics that offer opportunities to contribute new insights or approaches to the existing body of knowledge.

Personal Interest

Select a topic that aligns with your passion and curiosity, as it will drive your motivation and engagement throughout the research process.

Expertise and Skills

Consider your existing knowledge and expertise in specific areas of computer science that can be leveraged for the chosen topic.

Collaboration Potential

Evaluate the potential for collaboration with other researchers or institutions to enhance the research outcomes.

Resources and Tools for Exploring Computer Science Research Topics

Academic journals and conferences.

Explore recent publications in computer science journals and conference proceedings to identify trending topics and ongoing research.

Research Databases

Utilize online databases like IEEE Xplore, ACM Digital Library, and Google Scholar to access a vast collection of research papers and articles.

Research Communities and Forums

Engage with online communities, forums, and social media groups focused on computer science research to exchange ideas and gather insights.

Academic Advisors and Experts

Consult with academic advisors, professors, and experts in the field who can provide guidance and suggest potential research topics.

Research Funding Agencies

Explore research funding opportunities and programs offered by government agencies, foundations, and industry organizations to support your research endeavors.

Importance of Aligning the Topic with Personal Interests and Expertise

It is crucial to select a research topic that aligns with your personal interests and expertise. A topic that resonates with you will keep you motivated and engaged throughout the research journey. Your existing knowledge and skills in specific areas of computer science will serve as a solid foundation for conducting in-depth research and making meaningful contributions. By pursuing a topic you are passionate about, you are more likely to enjoy the research process and achieve better outcomes.

Choosing the right research topic requires careful consideration of various factors, including relevance, feasibility, personal interest, and expertise. By conducting thorough research and exploring available resources and tools, you can identify a topic that not only aligns with your goals and interests but also contributes to the advancement of computer science.

In this guide, we have explored a range of research topics in computer science, covering fundamental areas, emerging trends, interdisciplinary domains, and specific industries. These topics highlight the diverse and dynamic nature of computer science research, offering opportunities for innovation, impact, and collaboration.

By considering factors such as relevance, feasibility, personal interest, and expertise, researchers can choose a research topic that aligns with their goals and aspirations. It is crucial to select a topic that not only addresses current challenges but also resonates with your passion and curiosity. This will fuel your motivation and drive throughout the research journey.

As computer science continues to evolve rapidly, it is essential to stay updated with the latest advancements, research papers, and conferences in the field. Engaging with academic communities, attending conferences, and leveraging online resources and tools will help you explore new avenues, connect with experts, and contribute to the ever-expanding knowledge base.

Remember, research is a collaborative endeavor, and by sharing your insights, findings, and innovations, you can contribute to the collective progress of computer science. Embrace the opportunity to make a difference and push the boundaries of knowledge in this exciting field.

So, go forth with enthusiasm, explore the depths of computer science research, and unlock new possibilities that shape the future of technology. Your contributions have the potential to revolutionize industries, improve lives, and pave the way for a more advanced and connected world. Happy researching!

Frequently Asked Questions

How do i narrow down my research topic within computer science.

Start by identifying your areas of interest and expertise within computer science. Then, consider the relevance, feasibility, and potential impact of various topics. Consult with your advisors, conduct literature reviews, and explore existing research to refine and narrow down your focus.

Can I change my research topic during my research journey?

Yes, it is not uncommon for researchers to refine or change their research topic as they progress. It is important to stay flexible and open to new possibilities. However, ensure that any changes align with your research objectives and are approved by your advisors or research committee.

How can I find research collaborators for my topic?

Engage in research communities, attend conferences, and network with other researchers to find potential collaborators. You can also reach out to experts in your field or join online platforms dedicated to connecting researchers. Collaboration can bring diverse perspectives, enhance the quality of research, and foster meaningful partnerships.

How can I stay updated with the latest advancements in computer science research?

Subscribe to reputable academic journals, conference proceedings, and research newsletters. Follow influential researchers and organizations on social media platforms to receive updates on the latest research trends. Attend conferences, workshops, and seminars to interact with experts and learn about cutting-edge research.

What if I encounter challenges or roadblocks in my research?

Research often comes with challenges. Seek guidance from your advisors, mentors, or colleagues who can provide insights and solutions. Collaborate with others in your research community to leverage collective knowledge and support. Remember, challenges can lead to valuable learning experiences and breakthroughs.

How can I ensure the ethical conduct of my research?

Familiarize yourself with ethical guidelines and principles relevant to your research area. Obtain necessary approvals and permissions, especially when involving human subjects or sensitive data. Maintain transparency, integrity, and respect for intellectual property rights throughout your research process.

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101 Best Computer Science Topics for 2023

Any student will know the difficulty that comes with developing and choosing a great topic in computer science. Generally speaking, a good topic should be original, interesting, and challenging. It should push the limits of the field of study while still adequately answering the main questions brought on by the study.

We understand the stress that this may cause students, which is why we’ve dedicated our time to search the web and print resources to find the latest computer science topics that create the biggest waves in the field. Here’s the list of the top computer science research topics for 2023 you can use for an essay or senior thesis :

AP Computer Science Topics for Students Entering College

How has big data impacted the way small businesses conduct market research?
Does machine learning negatively impact the way neurons in the brain work?
Did biotech change how medicine is administered to patients?
How is human perception affected by virtual reality technologies?
How can education benefit from using virtual reality in learning?
Are quantum computers the way of the future or are they just a fad?
Has the Covid-19 pandemic delayed advancements in computer science?

Computer Science Research Paper Topics for High School

How successful has distance learning computer tech been in the time of Covid-19?
Will computer assistance in businesses get rid of customer service needs?
How has encryption and decryption technology changed in the last 20 years?
Can AI impact computer management and make it automated?
Why do programmers avoid making a universal programming language?
How important are human interactions with computer development?
How will computers change in the next five to ten years?

Controversial Topics in Computer Science for Grad Students

What is the difference between math modeling and art?
How are big-budget Hollywood films being affected by CGI technologies?
Should students be allowed to use technology in classrooms other than comp science?
How important is it to limit the amount of time we spend using social media?
Are quantum computers for personal or home use realistic?
How are embedded systems changing the business world?
In what ways can human-computer interactions be improved?

Computer Science Capstone Project Ideas for College Courses

What are the physical limitations of communication and computation?
Is SCRUM methodology still viable for software development?
Are ATMs still secure machines to access money or are they a threat?
What are the best reasons for using open source software?
The future of distributed systems and its use in networks?
Has the increased use of social media positively or negatively affected our relationships?
How is machine learning impacted by artificial intelligence?

Interesting Computer Science Topics for College Students

How has Blockchain impacted large businesses?
Should people utilize internal chips to track their pets?
How much attention should we pay to the content we read on the web?
How can computers help with human genes sequencing?
What can be done to enhance IT security in financial institutions?
What does the digitization of medical fields mean for patients’ privacy?
How efficient are data back-up methods in business?

Hot Topics in Computer Science for High School Students

Is distance learning the new norm for earning postgraduate degrees?
In reaction to the Covid-19 pandemic should more students take online classes?
How can game theory aid in the analysis of algorithms?
How can technology impact future government elections?
Why are there fewer females in the computer science field?
Should the world’s biggest operating systems share information?
Is it safe to make financial transactions online?

Ph.D. Research Topics in Computer Science for Grad Students

How can computer technology help professional athletes improve performance?
How have Next Gen Stats changed the way coaches game plan?
How has computer technology impacted medical technology?
What impact has MatLab software had in the medical engineering field?
How does self-adaptable application impact online learning?
What does the future hold for information technology?
Should we be worried about addiction to computer technology?

Computer Science Research Topics for Undergraduates

How has online sports gambling changed IT needs in households?
In what ways have computers changed learning environments?
How has learning improved with interactive multimedia and similar technologies?
What are the psychological perspectives on IT advancements?
What is the balance between high engagement and addiction to video games?
How has the video gaming industry changed over the decades?
Has social media helped or damaged our communication habits?

Research Paper Topics in Computer Science

What is the most important methodology in project planning?
How has technology improved people’s chances of winning in sports betting?
How has artificial technology impacted the U.S. economy?
What are the most effective project management processes in IT?
How can IT security systems help the practice of fraud score generation?
Has technology had an impact on religion?
How important is it to keep your social networking profiles up to date?

Computer Science Thesis Topics for College Students

How can logic and sets be used in computing?
How has online gambling impacted in-person gambling?
How did the 5-G network generation change communication?
What are the biggest challenges to IT due to Covid-19?
Do you agree that assembly language is a new way to determine data-mine health?
How can computer technology help track down criminals?
Is facial recognition software a violation of privacy rights?

Quick and Easy Computer Science Project Topics

Why do boys and girls learn the technology so differently?
How effective are computer training classes that target young girls?
How does technology affect how medicines are administered?
Will further advancements in technology put people out of work?
How has computer science changed the way teachers educate?
Which are the most effective ways of fighting identify theft?

Excellent Computer Science Thesis Topic Ideas

What are the foreseeable business needs computers will fix?
What are the pros and cons of having smart home technology?
How does computer modernization at the office affect productivity?
How has computer technology led to more job outsourcing?
Do self-service customer centers sufficiently provide solutions?
How can a small business compete without updated computer products?

Computer Science Presentation Topics

What does the future hold for virtual reality?
What are the latest innovations in computer science?
What are the pros and cons of automating everyday life?
Are hackers a real threat to our privacy or just to businesses?
What are the five most effective ways of storing personal data?
What are the most important fundamentals of software engineering?

Even More Topics in Computer Science

In what ways do computers function differently from human brains?
Can world problems be solved through advancements in video game technology?
How has computing helped with the mapping of the human genome?
What are the pros and cons of developing self-operating vehicles?
How has computer science helped developed genetically modified foods?
How are computers used in the field of reproductive technologies?

Our team of academic experts works around the clock to bring you the best project topics for computer science student. We search hundreds of online articles, check discussion boards, and read through a countless number of reports to ensure our computer science topics are up-to-date and represent the latest issues in the field. If you need assistance developing research topics in computer science or need help editing or writing your assignment, we are available to lend a hand all year. Just send us a message “ help me write my thesis ” and we’ll put you in contact with an academic writer in the field.

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EECS’ research covers a wide variety of topics in electrical engineering , computer science , and artificial intelligence and decision-making .

The future of our society is interwoven with the future of data-driven thinking—most prominently, artificial intelligence is set to reshape every aspect of our lives. Research in this area studies the interface between AI-driven systems and human actors, exploring both the impact of data-driven decision-making on human behavior and experience, and how AI technologies can be used to improve access to opportunities. This research combines a variety of areas including AI, machine learning, economics, social psychology, and law.

Our goal is to develop AI technologies that will change the landscape of healthcare. This includes early diagnostics, drug discovery, care personalization and management. Building on MIT’s pioneering history in artificial intelligence and life sciences, we are working on algorithms suitable for modeling biological and clinical data across a range of modalities including imaging, text and genomics.

Our research covers a wide range of topics of this fast-evolving field, advancing how machines learn, predict, and control, while also making them secure, robust and trustworthy. Research covers both the theory and applications of ML. This broad area studies ML theory (algorithms, optimization, …), statistical learning (inference, graphical models, causal analysis, …), deep learning, reinforcement learning, symbolic reasoning ML systems, as well as diverse hardware implementations of ML.

We develop the technology and systems that will transform the future of biology and healthcare. Specific areas include biomedical sensors and electronics, nano- and micro-technologies, imaging, and computational modeling of disease.

We develop the next generation of wired and wireless communications systems, from new physical principles (e.g., light, terahertz waves) to coding and information theory, and everything in between.

We bring some of the most powerful tools in computation to bear on design problems, including modeling, simulation, processing and fabrication.

We design the next generation of computer systems. Working at the intersection of hardware and software, our research studies how to best implement computation in the physical world. We design processors that are faster, more efficient, easier to program, and secure. Our research covers systems of all scales, from tiny Internet-of-Things devices with ultra-low-power consumption to high-performance servers and datacenters that power planet-scale online services. We design both general-purpose processors and accelerators that are specialized to particular application domains, like machine learning and storage. We also design Electronic Design Automation (EDA) tools to facilitate the development of such systems.

Educational technology combines both hardware and software to enact global change, making education accessible in unprecedented ways to new audiences. We develop the technology that makes better understanding possible.

Our research spans a wide range of materials that form the next generation of devices, and includes groundbreaking research on graphene & 2D materials, quantum computing, MEMS & NEMS, and new substrates for computation.

Our research focuses on solving challenges related to the transduction, transmission, and control of energy and energy systems. We develop new materials for energy storage, devices and power electronics for harvesting, generation and processing of energy, and control of large-scale energy systems.

The shared mission of Visual Computing is to connect images and computation, spanning topics such as image and video generation and analysis, photography, human perception, touch, applied geometry, and more.

The focus of our research in Human-Computer Interaction (HCI) is inventing new systems and technology that lie at the interface between people and computation, and understanding their design, implementation, and societal impact.

This broad research theme covered activities across all aspects of systems that process information, and the underlying science and mathematics, and includes communications, networking & information theory; numerical and computational simulation and prototyping; signal processing and inference; medical imaging; data science, statistics and inference.

Our field deals with the design and creation of sophisticated circuits and systems for applications ranging from computation to sensing.

Our research focuses on the creation of materials and devices at the nano scale to create novel systems across a wide variety of application areas.

Our research encompasses all aspects of speech and language processing—ranging from the design of fundamental machine learning methods to the design of advanced applications that can extract information from documents, translate between languages, and execute instructions in real-world environments.

Our work focuses on materials, devices, and systems for optical and photonic applications, with applications in communications and sensing, femtosecond optics, laser technologies, photonic bandgap fibers and devices, laser medicine and medical imaging, and millimeter-wave and terahertz devices.

Research in this area focuses on developing efficient and scalable algorithms for solving large scale optimization problems in engineering, data science and machine learning. Our work also studies optimal decision making in networked settings, including communication networks, energy systems and social networks. The multi-agent nature of many of these systems also has led to several research activities that rely on game-theoretic approaches.

We develop new approaches to programming, whether that takes the form of programming languages, tools, or methodologies to improve many aspects of applications and systems infrastructure.

Our work focuses on developing the next substrate of computing, communication and sensing. We work all the way from new materials to superconducting devices to quantum computers to theory.

Our research focuses on robotic hardware and algorithms, from sensing to control to perception to manipulation.

Our research is focused on making future computer systems more secure. We bring together a broad spectrum of cross-cutting techniques for security, from theoretical cryptography and programming-language ideas, to low-level hardware and operating-systems security, to overall system designs and empirical bug-finding. We apply these techniques to a wide range of application domains, such as blockchains, cloud systems, Internet privacy, machine learning, and IoT devices, reflecting the growing importance of security in many contexts.

Signal processing focuses on algorithms and hardware for analyzing, modifying and synthesizing signals and data, across a wide variety of application domains. As a technology it plays a key role in virtually every aspect of modern life including for example entertainment, communications, travel, health, defense and finance.

From distributed systems and databases to wireless, the research conducted by the systems and networking group aims to improve the performance, robustness, and ease of management of networks and computing systems.

Our theoretical research includes quantification of fundamental capabilities and limitations of feedback systems, inference and control over networks, and development of practical methods and algorithms for decision making under uncertainty.

Theory of Computation (TOC) studies the fundamental strengths and limits of computation, how these strengths and limits interact with computer science and mathematics, and how they manifest themselves in society, biology, and the physical world.

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12 Most Emerging Research Areas in Computer Science in 2021

By: P. Chaudhary, B. Gupta

Artificial Intelligence and Robotics

Artificial Intelligence and Robotics [1, 2] field aims at developing computational system that are intelligent in decision making, planning, object recognition, and other complex computational tasks that require minimum human intervention. This field emphasizes upon the development of cognitive algorithms for a variety of domains including e-commerce, healthcare, transport, manufacturing, gaming, defense industry, logistics, to name a few. It includes the application of popular emerging technologies such as Deep leaning, machine learning, Natural language processing (NLP), robotics, evolutionary algorithms, statistical inference, probabilistic methods, and computer vision. Some of the eminent research areas includes the following:

Knowledge representation and reasoning
Estimation theory
Mobility mechanisms
Multi-agent negotiation
Intelligent agents
Semantic segmentation
Assistive robotics in medical diagnosis
Robot perception and learning
Motion planning and control
Autonomous vehicles
Personal assistive robots
Search and information retrieval
Speech and language recognition
Fuzzy and neural system
Intelligent embedded system in industries
Object detection and capturing
Intelligent information systems

2. Big Data Analytics

Big data analytics [3, 4] research field involves design and development of techniques/algorithms/frameworks to explore the large amount of data to fulfill organization’s objectives. This area includes mathematical, statistical and graphical approaches to mine useful knowledge patterns from heterogeneous raw data. It is one of the potential and emerging research domains as almost every organization is attempting to utilize available data to enhance their productivity and services to their customers. Some of the distinguished research areas are following:

Predictive analysis
Data capturing and transmission
Parallel Data processing
Uncertainty in data
Data anonymization methods
Data processing in distributed environment
Privacy protecting techniques
Semantic analysis on social media
Intelligent traffic surveillance
Topological data analysis

3. Biometrics and Computational Biology

This field embraces enormous potential for researchers as it amalgamates multiple research areas including big data, image processing, biological science, data mining, and machine learning. This field emphasizes on the designing and development of computational techniques for processing biological data [5, 6]. Some of the potential research areas includes:

Structure and sequence analysis algorithms
Protein structure anticipation
Data modeling of scientific applications
Virtual screening
Brain image analysis using data mining approaches
Design predictive models for severe disease analysis
Molecular structure modeling and analysis
Brain-machine interfaces
Computational neuroscience

4. Data Mining and Databases

This field motivates research on designing vital methods, prototype schemes and applications in data mining and databases. This field ensembles all methods, techniques, and algorithms used for extracting knowledgeable information from the available heterogenous raw data [7, 8]. It enables classification, characterization, searching and clustering different datasets from wide range of domains including e-commerce, social media, healthcare, to name a few. This field demands parallel and distributed processing of data as it operates on massive quantity of data. It integrates various research domains including artificial intelligence, big data analytics, data mining, database management system, and bioinformatics. Some of the eminent research areas comprises as follows:

Distributed data mining
Multimedia storage and retrieval
Data clustering
Pattern matching and analysis
High-dimensional data modeling
Spatial and scientific data mining for sensor data
Query interface for text/image processing
Scalable data analysis and query processing
Metadata management
Graph database management and analysis system for social media
Interactive data exploration and visualization
Secure data processing

5. Internet of Things (IoTs)

Internet of Things has transformed the lives of people through exploring new horizons of networking. It connects physical objects with the internet as per the application to serve the user. This field carries enormous potential in different research areas related to the IoT and its interrelated research domains [9, 10]. These areas include as follows:

IoT network infrastructure design
Security issues in IoT
Architectural issues in Embedded system
Adaptive networks for IoT
Service provisioning and management in IoT
Middleware management in IoT
Handling Device Interoperability in IoT
Scalability issues in IoT
Privacy and trust issues in IoT
Data storage and analysis in IoT networks
Integration of IoT with other emerging technologies such as fog computing, SDN, Blockchain, etc.
Context and location awareness in IoT networks
Modeling and management of IoT applications
Task scheduling in IoT networks
Resource allotment among smart devices in IoT networks.

6. High-Performance Computing

This field encourage the research in designing and development of parallel algorithms/techniques for multiprocessor and distributed systems. These techniques are efficient for data and computationally exhaustive programs like data mining, optimization, super computer application, graph portioning, to name a few [11, 12]. Some of the eminent research challenges includes the following:

Information retrieval methods in cloud storage
Graph mining in social media networks
Distributed and parallel computing methods
Development of architecture aware algorithms
Big data analytics methods on GPU system
Designing of parallel algorithms
Designing of algorithms for Quantum computing

7. Blockchain and Decentralized Systems

This field [13, 14] revolutionize the digital world through processing network information without any central authority. This field is an emerging computing paradigm and motivates the design and development of algorithms that operate in decentralized environment. These techniques provide security, robustness and scalability in the network. Some of the eminent research areas includes the following:

Enhancing IoT security using blockchain
Precision agriculture and blockchain
Social blockchain networks
Blockchain based solutions for intelligent transportation system
Security and privacy issues in blockchain networks
Digital currencies and blockchain
Blockchain and 5G/6G communication networks
Integration of cloud/fog computing with blockchain
Legislation rules and policies for blockchain
Artificial Intelligence for blockchain system

8. Cybersecurity

With the development of new technology such as IoT, attackers have wider attack surface to halt the normal functioning of any network. Attackers may have several intentions to trigger cyber-attacks either against an individual person, organization, and/or a country. Now-a-days, we are living in a digital world where everything is connected is to the internet, so we are prone to some form of security attacks [15, 16]. This field carries massive potential for research on different techniques/methods to defend against these attacks. Some of the emerging research areas comprise the following:

Intrusion detection system
Applied cryptography
Privacy issues in RFID system
Security challenges in IoT system
Malware detection in cloud computing
Security and privacy issues in social media
Wireless sensor network security
Mobile device security
Lawa and ethics in cybersecurity
Cyber physical system security
Software defined network security
Security implications of the quantum computing
Blockchain and its security
AI and IoT security
Privacy issues in big data analytics
Phishing detection in finance sector

9. AI and Cyber Physical System

Specifically, Cyber physical system integrates computation and physical methods whose functionalities is determined by both physical and cyber component of the system. Research in this area motivates the development of tools, techniques, algorithms and theories for the CPS and other interrelated research domains [17, 18]. Research topics includes the following:

Human computer interaction
Digital design of CPS interfaces
Embedded system and its security
Industrial Interne to things
Automation in manufacturing industries
Robotics in healthcare sector
Medical informatics
AI, robotics and cyber physical system
Robot networks
Cognitive computing and CPS

10. Networking and Embedded Systems

This field [19, 20] encourages research on the designing of contemporary theories and approaches, effective and scalable methods and protocols, and innovative network design structure and services. These mechanisms improve the reliability, availability, security, privacy, manageability of current and future network and embedded systems. Research in this domain comprises of following topics:

Cyber physical system
Design of novel network protocols
Cognitive radio networks
Network security for lightweight and enterprise networks
Resource allocation schemes in resource-constrained networks
Network coding
Energy efficient protocols for wireless sensor networks
AI and embedded system
Embedded system for precision agriculture

11. Computer Vision and Augmented Reality

Computer vision [21, 22] is a multidisciplinary field that make computer system to understand and extract useful information from digital images and videos. This field motivates the research in designing the tools and techniques for understanding, processing, extracting, and storing, analyzing the digital images and videos. It embraces multiple domains such as image processing, artificial intelligence, pattern recognition, virtual reality, augmented reality, semantic structuring, statistics, and probability. Some of the eminent research topics includes the following:

Computer vision for autonomous robots
Object detection in autonomous vehicles
Object detection and delineation in UAVs network.
Biomedical image analysis
Augmented reality in gaming
Shape analysis in digital images
Computer vision for forensics
Robotics navigation
Deep learning techniques for computer vision
Automation in manufacturing sector
3D object recognition and tracking

12. Wireless Networks and Distributed Systems

The research in this field emphasizes on the developments of techniques that facilitate communication and maintain coordination among distributed nodes in a network [23, 24]. It is a broad area that embraces numerous domains including cloud computing, wireless networks, mobile computing, big data, and edge computing. Some of the eminent research topics includes the following:

Message passing models in distributed system
Parallel distributed computing
Fault tolerance and load balancing
Dynamic resource allocation in distributed system
Resource discovery and naming
Low-latency consistency protocols
Designing of consensus protocols
Efficient communication protocols in distributed system
Security issues in distributed networks
Privacy and trust models
Optimization of distributed storage
Distributed and federated machine learning

[1] Wisskirchen, G., Biacabe, B. T., Bormann, U., Muntz, A., Niehaus, G., Soler, G. J., & von Brauchitsch, B. (2017). Artificial intelligence and robotics and their impact on the workplace . IBA Global Employment Institute, 11(5), 49-67. [2] Kortenkamp, D., Bonasso, R. P., & Murphy, R. (Eds.). (1998). Artificial intelligence and mobile robots: case studies of successful robot systems. MIT Press. [3] Dai, H. N., Wang, H., Xu, G., Wan, J., & Imran, M. (2020). Big data analytics for manufacturing internet of things: opportunities, challenges and enabling technologies . Enterprise Information Systems, 14(9-10), 1279-1303. [4] Müller, O., Junglas, I., Vom Brocke, J., & Debortoli, S. (2016). Utilizing big data analytics for information systems research: challenges, promises and guidelines . European Journal of Information Systems, 25(4), 289-302. [5] Waterman, M. S. (2018). Introduction to computational biology: maps, sequences and genomes. Chapman and Hall/CRC. [6] Imaoka, H., Hashimoto, H., Takahashi, K., Ebihara, A. F., Liu, J., Hayasaka, A., … & Sakurai, K. (2021). The future of biometrics technology: from face recognition to related applications. APSIPA Transactions on Signal and Information Processing, 10. [7] Zhu, X., & Davidson, I. (Eds.). (2007). Knowledge Discovery and Data Mining: Challenges and Realities: Challenges and Realities . Igi Global. [8] Tseng, L., Yao, X., Otoum, S., Aloqaily, M., & Jararweh, Y. (2020). Blockchain-based database in an IoT environment: challenges, opportunities, and analysis. Cluster Computing, 23(3), 2151-2165. [9] Stoyanova, M., Nikoloudakis, Y., Panagiotakis, S., Pallis, E., & Markakis, E. K. (2020). A survey on the internet of things (IoT) forensics: challenges, approaches, and open issues. IEEE Communications Surveys & Tutorials, 22(2), 1191-1221. [10] Nižetić, S., Šolić, P., González-de, D. L. D. I., & Patrono, L. (2020). Internet of Things (IoT): Opportunities, issues and challenges towards a smart and sustainable future. Journal of Cleaner Production, 274, 122877. [11] Hager, G., & Wellein, G. (2010). Introduction to high performance computing for scientists and engineers. CRC Press. [12] Wang, G. G., Cai, X., Cui, Z., Min, G., & Chen, J. (2017). High performance computing for cyber physical social systems by using evolutionary multi-objective optimization algorithm . IEEE Transactions on Emerging Topics in Computing, 8(1), 20-30. [13] Zheng, Z., Xie, S., Dai, H. N., Chen, X., & Wang, H. (2018). Blockchain challenges and opportunities: A survey. International Journal of Web and Grid Services, 14(4), 352-375. [14] Nguyen, D. C., Ding, M., Pham, Q. V., Pathirana, P. N., Le, L. B., Seneviratne, A., … & Poor, H. V. (2021). Federated learning meets blockchain in edge computing: Opportunities and challenges . IEEE Internet of Things Journal. [15] Tawalbeh, L. A., Muheidat, F., Tawalbeh, M., & Quwaider, M. (2020). IoT Privacy and security: Challenges and solutions. Applied Sciences, 10(12), 4102. [16] Boubiche, D. E., Athmani, S., Boubiche, S., & Toral-Cruz, H. (2021). Cybersecurity Issues in Wireless Sensor Networks: Current Challenges and Solutions. Wireless Personal Communications, 117(1). [17] Gupta, R., Tanwar, S., Al-Turjman, F., Italiya, P., Nauman, A., & Kim, S. W. (2020). Smart contract privacy protection using ai in cyber-physical systems: Tools, techniques and challenges. IEEE Access, 8, 24746-24772. [18] Kravets, A. G., Bolshakov, A. A., & Shcherbakov, M. V. (2020). Cyber-physical Systems: Industry 4.0 Challenges . Springer. [19] Duan, Q., Wang, S., & Ansari, N. (2020). Convergence of networking and cloud/edge computing: Status, challenges, and opportunities. IEEE Network, 34(6), 148-155. [20] Wang, C. X., Di Renzo, M., Stanczak, S., Wang, S., & Larsson, E. G. (2020). Artificial intelligence enabled wireless networking for 5G and beyond: Recent advances and future challenges. IEEE Wireless Communications, 27(1), 16-23. [21] Chen, C. H. (Ed.). (2015). Handbook of pattern recognition and computer vision . World Scientific. [22] Esteva, A., Chou, K., Yeung, S., Naik, N., Madani, A., Mottaghi, A., … & Socher, R. (2021). Deep learning-enabled medical computer vision. NPJ digital medicine, 4(1), 1-9. [23] Farahani, B., Firouzi, F., & Luecking, M. (2021). The convergence of IoT and distributed ledger technologies (DLT): Opportunities, challenges, and solutions. Journal of Network and Computer Applications, 177, 102936. [24] Alfandi, O., Otoum, S., & Jararweh, Y. (2020, April). Blockchain solution for iot-based critical infrastructures: Byzantine fault tolerance. In NOMS 2020-2020 IEEE/IFIP Network Operations and Management Symposium (pp. 1-4). IEEE.

Cite this article:

P. Chaudhary, B. Gupta (2021) 12 Most Emerging Research Areas in Computer Science in 2021 , Insights2Techinfo, pp. 1

FAQ on this topic

Artificial Intelligence and Robotics, Big Data Analytics, Biometrics and Computational Biology, Data Mining and Databases, Internet of Things (IoTs), High-Performance Computing, Blockchain and Decentralized Systems,Cybersecurity

Big data research field involves design and development of techniques/algorithms/frameworks to explore the large amount of data to fulfill organization’s objectives. Some of the distinguished research areas are following: Data capturing and transmission, Parallel Data processing,Data anonymization methods,Data processing in distributed environment

Artificial Intelligence field aims at developing computational systems that are intelligent in decision making, planning, object recognition, and other complex computational tasks that require minimum human intervention. Some of the eminent research areas includes the following: Knowledge representation and reasoning Autonomous vehicles, Fuzzy and neural system, Intelligent information systems

Some of the eminent research areas comprises as follows:Distributed data mining, Multimedia storage and retrieval, Data clustering, Pattern matching and analysis, High-dimensional data modeling, Spatial and scientific data mining for sensor data.

The research areas in IoT include as follows: IoT network infrastructure design, Security issues in IoT,Architectural issues in Embedded system, Service provisioning and management in IoT, Middleware management in IoT

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224 Research Topics on Technology & Computer Science

Are you new to the world of technology? Do you need topics related to technology to write about? No worries, Custom-writing.org experts are here to help! In this article, we offer you a multitude of creative and interesting technology topics from various research areas, including information technology and computer science. So, let’s start!

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🔝 Top 10 Topics

👋 Introduction

💾 Top 10 Computer Science Topics

⚙ Artificial Intelligence

💉 biotechnology, 📡 communications and media.

💻Computer Science & Engineering

🔋 Energy & Power Technologies

🍗 food technology, 😷 medical devices & diagnostics, 💊 pharmaceutical technologies.

🚈 Transportation

✋ Conclusion

🔍 references, 🔝 top 10 technology topics.

The difference between VR and AR
Is genetic engineering ethical?
Can digital books replace print ones?
The impact of virtual reality on education
5 major fields of robotics
The risks and dangers of biometrics
Nanotechnology in medicine
Digital technology’s impact on globalization
Is proprietary software less secure than open-source?
The difference between deep learning and machine learning

Is it a good thing that technologies and computer science are developing so fast? No one knows for sure. There are too many different opinions, and some of them are quite radical! However, we know that technologies have changed our world once and forever. Computer science affects every single area of people’s lives.

Just think about Netflix . Can you imagine that 24 years ago it didn’t exist? How did people live without it? Well, in 2024, the entertainment field has gone so far that you can travel anywhere while sitting in your room. All you would have to do is just order a VR (virtual reality) headset. Moreover, personal computers give an unlimited flow of information, which has changed the entire education system.

Every day, technologies become smarter and smaller. A smartphone in your pocket may be as powerful as your laptop. No doubt, the development of computer science builds our future. It is hard to count how many research areas in technologies and computer science are there. But it is not hard to name the most important of them.

Artificial intelligence tops the charts, of course. However, engineering and biotechnology are not far behind. Communications and media are developing super fast as well. The research is also done in areas that make our lives better and more comfortable. The list of them includes transport, food and energy, medical, and pharmaceutical areas.

So check out our list of 204 most relevant computer science research topics below. Maybe one of them will inspire you to do revolutionary research!

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💾 Top 10 Computer Science Research Topics

💡 technologies & computer science: research ideas.

Many people probably picture robots from the movie “I, Robot” when they hear about artificial intelligence. However, it is far from the truth.

AI is meant to be as close to a rational way of thinking as possible. It uses binary logic (just like computers) to help solve problems in many areas. Applied AI is only aimed at one task. A generalized AI branch is looking into a human-like machine that can learn to do anything.

Applied AI already helps researchers in quantum physics and medicine. You deal with AI every day when online shops suggest some items based on your previous purchases. Siri and self-driving cars are also examples of applied AI.

Generalized AI is supposed to be a copy of multitasking human intelligence. However, it is still in the stage of development. Computer technology has yet to reach the level necessary for its creation.

One of the latest trends in this area is improving healthcare management. It is done through the digitalization of all the information in hospitals and even helping diagnose the patients.

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Also, privacy issues and facial recognition technologies are being researched. For example, some governments collect biometric data to reduce and even predict crime.

Research Topics on Artificial Intelligence Technology

Since AI development is exceptionally relevant nowadays, it would be smart to invest your time and effort into researching it. Here are some ideas on artificial intelligence research topics that you can look into:

What areas of life machine learning are the most influential?
How to choose the right algorithm for machine learning ?
Supervised vs. unsupervised machine learning : compare & contrast
Reinforcement machine learning algorithms
Deep learning as a subset of machine learning
Deep learning & artificial neural networks
How do artificial neural networks work?
A comparison of model-free & model-based reinforcement learning algorithms
Reinforcement learning: single vs. multi-agent
How do social robots interact with humans?
Robotics in NASA
Natural language processing: chatbots
How does natural language processing produce natural language?
Natural language processing vs. machine learning
Artificial intelligence in computer vision
Computer vision application: autonomous vehicles
Recommender systems’ approaches
Recommender systems: content-based recommendation vs. collaborative filtering
Internet of things & artificial intelligence: the interconnection
How much data do the Internet of things devices generate?

Biotechnology uses living organisms to modify different products. Even the simple thing as baking bread is a process of biotechnology. However, nowadays, this area went as far as changing the organisms’ DNA. Genetics and biochemistry are also a part of the biotechnology area.

The development of this area allows people to cure diseases with the help of new medicines. In agriculture, more and more research is done on biological treatment and modifying plants. Biotechnology is even involved in the production of our groceries, household chemicals, and textiles.

There are many exciting trends in biotechnology now that carry the potential of changing our world! For example, scientists are working on creating personalized drugs. This is feasible once they apply computer science to analyze people’s DNA.

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Also, thanks to using new technologies, doctors can collect exact data and provide the patients with correct diagnosis and treatment. Now, you don’t even need to leave your place to get a doctor’s check-up. Just use telehealth!

Data management is developing in the biotechnology area as well. Thanks to that, doctors and scientists can store and access a tremendous amount of information.

The most exciting is the fact that new technology enables specialists to assess genetic information to treat and prevent illnesses! It may solve the problem of some diseases that were considered untreatable before.

Research Topics on Biotechnology

You can use the following examples of research questions on biotechnology for presentation or even a PhD paper! Here is a wide range of topics on biotechnology and its relation to agriculture, nanotechnology, and many more:

Self-sufficient protein supply and biotechnology in farming
Evaporation vs. evapotranspiration
DNA cloning and a southern blot
Pharmacogenetics & personalized drugs
Is cloning “playing God”?
Pharmacogenetics : cancer medicines
How much can we control our genetics, at what point do we cease to be human?
Bio ethics and stem cell research
Genetic engineering: gene therapy
The potential benefits of genetic engineering
Genetic engineering: dangers and opportunities
Mycobacterium tuberculosis : counting the proteins
Plant genetic enhancement: developing resistance to scarcity
Y-chromosome genotyping: the case of South Africa
Agricultural biotechnology: GMO crops
How are new vaccines developed?
Nanotechnology in treating HIV
Allergenic potential & biotechnology
Whole-genome sequencing in biotechnology
Genes in heavy metal tolerance: an overview
Food biotechnology & food-borne illnesses
How to eliminate heat-resistant microorganisms with ultraviolet?
High-throughput screening & biotechnology
How do new food processing technologies affect bacteria related to Aspalathus Linearis?
Is sweet sorghum suitable for the production of bioethanol in Africa?
How can pesticides help to diagnose cancer?
How is embelin used to prevent cancer?

One of the first areas that technologies affected was communications and media. People from the last century couldn’t have imagined how easy it would be to get connected with anyone! Internet connection starts appearing even in the most remote places.

Nowadays, media is used not only for social interaction but for business development and educational purposes as well. You can now start an entirely online business or use special tools to promote the existing one. Also, many leading universities offer online degrees.

In communications and media, AI has been playing the role of enhancement recently. The technology helps create personalized content for always demanding consumers.

Developing media also create numerous job opportunities. For instance, recently, an influencer has become a trending career. Influencers always use the most relevant communication tools available. At the moment, live videos and podcasting are on the top.

Now, you just need to reach your smartphone to access all the opportunities mentioned above! You can apply for a college, find a job, or reach out to all your followers online. It is hard to imagine how far communication and media can go…

Communications and Media Technology Research Topics

There are quite a few simple yet exciting ideas for media and communications technology research topics. Hopefully, you will find THE ONE amongst these Information and Communications Technology (ICT) research proposal topics:

New media: the importance of ethics in the process of communication
The development of computer-based communication over the last decade
How have social media changed communication?
Media during the disasters : increasing panic or helping reduce it?
Authorities’ media representations in different countries: compare & contrast
Do people start preferring newspapers to new media again?
How has the Internet changed media?
Communication networks
The impact of social media on super bowl ads
Communications: technology and personal contact
New content marketing ideas
Media exposure and its influence on adolescents
The impact of mass media on personal socialization
Internet and interactive media as an advertising tool
Music marketing in a digital world
How do people use hype in the media?
Psychology of videoblog communication
Media & the freedom of speech
Is it possible to build trustful relationships in virtual communication?
How to maintain privacy in social media ?
Communication technologies & cyberbullying
How has the interpersonal communication changed with the invention of computers?
The future of the communication technologies
Yellow journalism in new media
How enterprises use ICT to get a competitive advantage?
Healthcare and ICT
Can we live without mass media ?
Mass media and morality in the 21st century

💻 Computer Science & Engineering

If you have ever wondered how computers work, you better ask a professional in computer science and engineering. This major combines two different, yet interconnected, worlds of machines.

Computer science takes care of the computer’s brain. It usually includes areas of study, such as programming languages and algorithms. Scientists also recognize three paradigms in terms of the computer science field.

For the rationalist paradigm, computer science is a part of math. The technocratic paradigm is focused on software engineering, while the scientific one is all about natural sciences. Interestingly enough, the latter can also be found in the area of artificial intelligence!

On the other hand, computer engineering maintains a computer’s body – hardware and software. It relies quite heavily on electrical engineering. And only the combination of computer science and engineering gives a full understanding of the machine.

If talking about trends and innovations, artificial intelligence development is probably the main one in the area of computer science technology. Big data is the field that has been extremely popular in recent years.

Cybersecurity is and will be one of the leading research fields in our Information Age. The latest trend in computer science and engineering is also virtual reality.

Computer Science Research Topics

If you want to find a good idea for your thesis or you are just preparing for a speech, check out this list of research topics in computer science and engineering:

How are virtual reality & human perception connected?
The future of computer-assisted education
Computer science & high-dimensional data modeling
Computer science: imperative vs. declarative languages
The use of blockchain and AI for algorithmic regulations
Banking industry & blockchain technology
How does the machine architecture affect the efficiency of code?
Languages for parallel computing
How is mesh generation used for computational domains?
Ways of persistent data structure optimization
Sensor networks vs. cyber-physical system
The development of computer graphics: non-photorealistic rendering case
The development of the systems programming languages
Game theory & network economics
How can computational thinking affect science?
Theoretical computer science in functional analysis
The most efficient cryptographic protocols
Software security types: an overview
Is it possible to eliminate phishing?
Floating point & programming language

Without energy, no technological progress is possible. Scientists are continually working on improving energy and power technologies. Recently, efforts have been aimed at three main areas.

Developing new batteries and fuel types helps create less expensive ways of storing energy. For example, fuel cells can be used for passenger buses. They need to be connected to a source of fuel to work. However, it guarantees the constant production of electricity as long as they have fuel.

One of the potential trends of the next years is hydrogen energy storage. This method is still in the stage of development. It would allow the use of hydrogen instead of electricity.

A smart grid is another area that uses information technology for the most efficient use of energy. For instance, the first-generation smart grid tracks the movement of electric energy on the go and sends the information back. It is a great way to correct the consumption of energy in real-time. More development is also done on the issue of electricity generation. It aims at technologies that can produce power from the sources that haven’t been used. The trends in this area include second-generation biofuels and photovoltaic glass.

Energy Technologies Research Topics

Since humanity cannot be using fossil fuels forever, the research in the area of energy can be extremely fruitful. The following list of energy and power technology research paper topics can give you an idea of where to dig:

How can fuel cells be used for stationary power generation?
Lithium-ion vs. lithium-air batteries: energy density
Are lithium-air batteries better than gasoline ?
Renewable energy usage: advantages and disadvantages
The nuclear power usage in the UAE
India’s solar installations
Gas price increasing and alternative energy sources
How can methods of energy transformation be applied with hydrogen energy?
Is hydrogen energy our future?
Thermal storage & AC systems
How to load balance using smart grid?
Distributed energy generation to optimize power waste
Is the smart energy network a solution to climate change ?
The future of the tidal power
The possibility of 3D printing of micro stirling engines
How can robots be used to adjust solar panels to weather?
Advanced biofuels & algae
Can photovoltaic glass be fully transparent?
Third-generation biofuels : algae vs. crop-based
Space-based solar power: myth or reality of the future?
Can smaller nuclear reactors be more efficient?
Inertial confinement fusion & creal energy
Renewable energy technologies: an overview
How can thorium change the nuclear power field?

The way we get our food has changed drastically with the technological development. Manufacturers look for ways to feed 7.5 billion people more efficiently. And the demand is growing every year. Now technology is not only used for packaging, but for producing and processing food as well.

Introducing robots into the process of manufacturing brings multiple benefits to the producer. Not only do they make it more cost-efficient, but they also reduce safety problems.

Surprisingly enough, you can print food on the 3D printer now! This technology is applied to produce soft food for people who can’t chew. NASA decided to use it for fun as well and printed a pizza!

Drones now help farmers to keep an eye on crops from above. It helps them see the full picture and analyze the current state of the fields. For example, a drone can spot a starting disease and save the crop.

The newest eco trends push companies to become more environmentally aware. They use technologies to create safer packaging. The issue of food waste is also getting more and more relevant. Consumers want to know that nothing is wasted. Thanks to the new technologies, the excess food is now used more wisely.

Food Technology Research Topics

If you are looking for qualitative research topics about technology in the food industry, here is a list of ideas you don’t want to miss:

What machines are used in the food industry?
How do robots improve safety in butchery?
Food industry & 3D printing
3D printed food – a solution to help people with swallowing disorder?
Drones & precision agriculture
How is robotics used to create eco-friendly food packaging ?
Is micro packaging our future?
The development of edible cling film

Technology & food waste : what are the solutions?
Additives and preservatives & human gut microbiome
The effect of citric acid on the orange juice: physicochemical level
Vegetable oils in mass production: compare & contrast
Time-temperature indicators & food industry
Conventional vs. hydroponic farming
Food safety: a policy issue in agriculture today
How to improve the detection of parasites in food?
What are the newest technologies in the baking industry?
Eliminating byproducts in edible oils production
Cold plasma & biofilms
How good are the antioxidant peptides derived from plants?
Electronic nose in food industry and agriculture
The harm of polyphenols in food

Why does the life expectancy of people get higher and higher every year? One of the main aspects of it is the promotion of innovation in the medical area. For example, the development of equipment helps medical professionals to save many lives.

Thanks to information technology, the work is much more structured now in the medical area. The hospitals use tablets and the method of electronic medical records. It helps them to access and share the data more efficiently.

If talking about medical devices, emerging technologies save more lives than ever! For instance, operations done by robots are getting more and more popular. Don’t worry! Doctors are still in charge; they just control the robots from the other room. It allows operations to be less invasive and precise.

Moreover, science not only helps treat diseases but also prevent them! The medical research aims for the development of vaccines against deadly illnesses like malaria.

Some of the projects even sound more like crazy ideas from the future. But it is all happening right now! Scientists are working on the creation of artificial organs and the best robotic prosthetics.

All the technologies mentioned above are critical for successful healthcare management.

Medical Technology Research Topics

If you feel like saving lives is the purpose of your life, then technological research topics in the medical area are for you! These topics would also suit for your research paper:

How effective are robotic surgeries ?
Smart inhalers as the new solution for asthma treatment
Genetic counseling – a new way of preventing diseases?
The benefits of the electronic medical records
Erythrocytapheresis to treat sickle cell disease
Defibrillator & cardiac resynchronization therapy
Why do drug-eluting stents fail?
Dissolvable brain sensors: an overview
3D printing for medical purposes
How soon will we be able to create artificial organs?
Wearable technologies & healthcare
Precision medicine based on genetics
Virtual reality devices for educational purposes in medical schools
The development of telemedicine
Clustered regularly interspaced short palindromic repeats as the way of treating diseases
Nanotechnology & cancer treatment
How safe is genome editing?
The trends in electronic diagnostic tools development
The future of the brain-machine interface
How does wireless communication help medical professionals in hospitals?

In the past years, technologies have been drastically changing the pharmaceutical industry. Now, a lot of processes are optimized with the help of information technology. The ways of prescribing and distributing medications are much more efficient today. Moreover, the production of medicines itself has changed.

For instance, electronic prior authorization is now applied by more than half of the pharmacies. It makes the process of acquiring prior authorization much faster and easier.

The high price of medicines is the number one reason why patients stop using prescriptions. Real-time pharmacy benefit may be the solution! It is a system that gives another perspective for the prescribers. While working with individual patients, they will be able to consider multiple factors with the help of data provided.

The pharmaceutical industry also adopts some new technologies to compete on the international level. They apply advanced data analytics to optimize their work.

Companies try to reduce the cost and boost the effectiveness of the medicines. That is why they look into technologies that help avoid failures in the final clinical trials.

The constant research in the area of pharma is paying off. New specialty drugs and therapies arrive to treat chronic diseases. However, there are still enough opportunities for development.

Pharmaceutical Technologies Research Topics

Following the latest trends in the pharmaceutical area, this list offers a wide range of creative research topics on pharmaceutical technologies:

Electronic prior authorization as a pharmacy technological trend
The effectiveness of medication therapy management
Medication therapy management & health information exchanges
Electronic prescribing of controlled substances as a solution for drug abuse issue
Do prescription drug monitoring programs really work?
How can pharmacists help with meaningful use?
NCPDP script standard for specialty pharmacies
Pharmaceutical technologies & specialty medications
What is the patient’s interest in the real-time pharmacy?
The development of the vaccines for AIDS
Phenotypic screening in pharmaceutical researches
How does cloud ERP help pharmaceutical companies with analytics?
Data security & pharmaceutical technologies
An overview of the DNA-encoded library technology
Pharmaceutical technologies: antibiotics vs. superbugs
Personalized medicine: body-on-a-chip approach
The future of cannabidiol medication in pain management
How is cloud technology beneficial for small pharmaceutical companies?
A new perspective on treatment: medicines from plants
Anticancer nanomedicine: a pharmaceutical hope

🚈 Transportation Technologies

We used to be focused on making transportation more convenient. However, nowadays, the focus is slowly switching to ecological issues.

It doesn’t mean that vehicles can’t be comfortable at the same time. That is why the development of electric and self-driving cars is on the peak.

Transportation technologies also address the issues of safety and traffic jams. There are quite many solutions suggested. However, it would be hard for big cities to switch to the other systems fast.

One of the solutions is using shared vehicle phone applications. It allows reducing the number of private cars on the roads. On the other hand, if more people start preferring private vehicles, it may cause even more traffic issues.

The most innovative cities even start looking for more eco-friendly solutions for public transport. Buses are being replaced by electric ones. At the same time, the latest trend is using private electric vehicles such as scooters and bikes.

So that people use public transport more, it should be more accessible and comfortable. That is why the payment systems are also being updated. Now, all you would need is to download an app and buy a ticket in one click!

Transportation Technologies Research Topics

Here you can find the best information technology research topics related to transportation technologies:

How safe are self-driving cars ?
Electric vs. hybrid cars : compare & contrast
How to save your smart car from being hijacked?
How do next-generation GPS devices adjust the route for traffic?
Transportation technologies: personal transportation pods
High-speed rail networks in Japan
Cell phones during driving: threats and solutions
Transportation: electric cars effects
Teleportation: physics of the impossible
How soon we will see Elon Musk’s Hyperloop?
Gyroscopes as a solution for convenient public transportation
Electric trucks: the effect on logistics
Why were electric scooters banned in some cities in 2018?
Carbon fiber as an optional material for unit load devices
What are the benefits of the advanced transportation management systems?
How to make solar roadways more cost-effective?
How is blockchain applied in the transportation industry
Transportation technologies: an overview of the freight check-in
How do delivery companies use artificial intelligence?
Water-fueled cars: the technology of future or fantasy?
What can monitoring systems be used to manage curb space?
Inclusivity and accessibility in public transport: an overview
The development of the mobility-as-a-service

All in all, this article is a compilation of the 204 most interesting research topics on technology and computer science. It is a perfect source of inspiration for anyone who is interested in doing research in this area.

We have divided the topics by specific areas, which makes it easier for you to find your favorite one. There are 20 topics in each category, along with a short explanation of the most recent trends in the area.

You can choose one topic from artificial intelligence research topics and start working on it right away! There is also a wide selection of questions on biotechnology and engineering that are waiting to be answered.

Since media and communications are present in our everyday life and develop very fast, you should look into this area. But if you want to make a real change, you can’t miss on researching medical and pharmaceutical, food and energy, and transportation areas.

Of course, you are welcome to customize the topic you choose! The more creativity, the better! Maybe your research has the power to change something! Good luck, and have fun!

This might be interesting for you:

280 Good Nursing Research Topics & Questions
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Databases for Research & Education: Gale
The Complete Beginners’ Guide to Artificial Intelligence: Forbes
8 Best Topics for Research and Thesis in Artificial Intelligence: GeeksForGeeks
Technology Is Changing Transportation, and Cities Should Adapt: Harvard Business Review
Five Technology Trends: Changing Pharmacy Practice Today and Tomorrow (Pharmacy Times)
Recent papers in Technology: Academia
Research: Michigan Tech
What 126 studies say about education technology: MIT News
Top 5 Topics in Information Technology: King University Online
Research in Technology Education-Some Areas of Need: Virginia Tech
Undergraduate Research Topics: Department of Computer Science, Princeton University
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Biotechnology: Definition, Examples, & Applications (Britannica)
Medical Laboratory Science Student Research Projects: Rush University
Clinical Laboratory Science: Choosing a Research Topic (Library Resource Guide for FGCU Clinical Lab Science students)
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Thanks so much for this! Glad I popped by and I sure did find what I was looking for.

Thanks for your kind words, Sanny! We look forward to seeing you again!

Thank you very for the best topics of research across all science and art projects. The best thing that I am interested to is computer forensics and security specifically for IT students.

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Hello, glad to hear from you!

Computer science focuses on creating programs and applications, while information technology focuses on using computer systems and networks. What computer science jobs are there. It includes software developers, web developers, software engineers, and data scientists.

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Editorial article, editorial: 2021 editors' pick: computer science.

1 Department of Computer Science, University of Kaiserslautern, Kaiserslautern, Germany
2 Human Media Interaction Group, University of Twente, Enschede, Netherlands
3 School of Computer Science, McGill University, Montreal, QC, Canada
4 Department of Computer Science, Ca' Foscari University of Venice, Venice, Italy
5 Department of Electrical Engineering and Computer Science, University of Siegen, Siegen, Germany
6 Department of Computer Science, King's College London, London, United Kingdom
7 Department of Mathematics and Computer Science, Eindhoven University of Technology, Eindhoven, Netherlands

Editorial on the Research Topic 2021 editors' pick: Computer science

Introduction

We are pleased to highlight the following ten articles which appeared in Frontiers in Computer Science in 2021. We have chosen to feature contributions from the following sections of the journal: Computer Security, Computer Vision, Human-Media Interaction and Mobile and Ubiquitous Computing. Computer science has grown to be a multi-faceted field where some of the most exciting developments are at its intersection with other disciplines. The articles below cover a wide array of topics including human behavior, technologies for the visually impaired, color representation and perception, pattern formation, facial action coding, mobile sensors, gait recognition, and phishing and reentrancy attacks. These articles have all been widely read and collectively their content reflects the interdisciplinary nature of our journal and aspects of our vision for its future.

Human-media interaction

Three papers have been chosen from the Human-Media Interaction section. These papers focus on design rather than on (interaction) technology.

In a “Mini Review” Lemke and de Vries investigate how theories from psychology and the social sciences can help guide the design of persuasive technology. They focus on social comparison theory ( Festinger, 1954 ) in which one's abilities and opinions are compared to others. The research question underlying this review is how social comparison can be operationalized as part of persuasive technology and be used to develop a design prototype. Twelve research papers are discussed in detail, highlighting current trends and potential gaps.

In an “Original Research” paper Nwadiugwu and Nwadiugwu discuss leadership and mechanisms that influence followership in online knowledge-building communities. In-depth interviews were conducted with experienced leaders of a social media online platform and leadership theory ( Burns, 1978 ) was used to distinguish and discuss themes in leadership-followership relations. Design implications for knowledge-building in online communities, platforms, and the services they provide should follow from understanding the mechanisms involved.

In another “Original Research” paper of the Human-Media Interaction section, Angkananon et al. introduce a framework for designing accessible technologies for visually impaired people. The framework helps to identify user requirements and it provides technology suggestions that support the design stage. The framework has been developed and evaluated using various scenarios (shopping, crossing the road, attending a lecture, et cetera) with the participation of (novice) developers, accessibility experts and visually impaired people.

Mobile and ubiquitous computing

Two articles have been chosen from the Mobile and Ubiquitous Computing section. The first article ( Wang et al. ) addresses aspects of a recognition challenge to evaluate the efficacy of sensors in today's personal devices while the second ( Liu et al. ) provides a review of wearable devices in healthcare applications.

In a “Review” article Wang et al. observe that the number of sensors that have been embedded in our personal and wearable devices has increased at a staggering pace, leading to a lot of research investigating how robustly one can detect information about the devices' users hidden in these data, including their whereabouts, and performed activities. Over the past 3 years these authors have organized the Sussex-Huawei Locomotion-Transportation (SHL) Recognition Challenge as a way to galvanize these research activities from researchers across the world and support replicable and comparable evaluations in this field. The article discusses the various methods seen in the challenges and presents baseline results to compare the three challenges using a single processing pipeline.

In a “Mini-Review” article, Liu et al. discuss the role of wearable devices in healthcare applications where gait analysis proves useful for monitoring the effect of injuries, neurodegenerative diseases, and musculoskeletal disorders. In these scenarios sensors are used to record data from walking and running, from which spatiotemporal and kinematic variables are extracted. The ultimate goal is to provide useful technologies to support doctors carry out early diagnosis and also to help physicians working on patient rehabilitation.

Computer vision

Three articles have been chosen from the Computer Vision section. The first ( Koenderink et al. ) proposes fundamentally new ways to analyze color spaces, motivated by ecological considerations. The second ( Duin ) considers the classic problem of pattern recognition and perception. The third ( Niinuma et al. ) carries out a systematic evaluation of design choices when deep learning is applied to facial action coding.

In a fascinating “Original Research” article Koenderink et al. promote an ecological approach to understand representations and models of color spaces. Taking into account optical considerations such as the fact that radiant power is non-negative and reflectance factors are fractional, as well as daylight viewing conditions, they show rigorously that color is naturally described by a spectral tripartition. This insightful paper offers a unique and novel perspective on color as a property of objects in the physical world, and provides a unified way to treat color mixing, relations between scenes and image data and a host of other phenomena related to color and color perception.

In a philosophical “Review” article Duin develops and contrasts two opposing but related views of pattern formation and pattern understanding. The author considers whether patterns are elements of the external world in which we live, or whether they are an emergent property in the mind of an observer due to the manner in which that observer interacts with their surrounding environment. Duin contrasts these two opposing strategies where in one an experienced observer can directly perceive or recognize an object without reasoning while in the other an artificial perception system must consider actual physical sensors or measurements followed by explicit reasoning. This debate is developed in the context of a concrete example in pattern analysis: the classification of digital histopathology slides.

In a “Original Research” article Niinuma et al. rightly point out that current deep learning systems, which appear to dominate so many disciplines these days, and in particular classification and detection, also require a fair amount of fine tuning and parameter selection in application to particular datasets and specific problems. Considering the problem of facial action unit coding, and the Facial Expression Recognition and Analysis 2017 ( FERA, 2017 ) dataset, the authors carry out a systematic evaluation of design choices and their effects on performance. A particular consideration is robustness in the presence of pose variation or in application to new datasets. The authors offer a practical view of the relative importance of design choices in pre-training, including: feature alignment, model size selection, and optimizer details, and the overall effect on performance in facial action coding.

Computer security

Two articles have been chosen from the Computer Security Section. The first article ( Alkhalil et al. ) provides an in-depth review of current phishing attack strategies, while the second ( Alkhalifa et al. ) examines attacks in blockchain smart contracts and proposes potential algorithmic solutions to them.

In a “Review” article Alkhalil et al. investigate the current state of phishing and review existing phishing techniques. The authors characterize the types of phishing attacks based on several dimensions including attacker's types, vulnerabilities, threats, targets, attack mediums, and attacking techniques. They also focus on the lifecycle of phishing attacks, which in combination with the previous dimensions, provides an in-depth understanding of how phishing attacks work and can help the design and development of protection measures to counter those attacks.

In an “Original Research” article Alkhalifa et al. analyze the root cause of reentrancy attacks in Ethereum blockchain and propose a solution to improve the cybersecurity of smart contracts against those attacks. The proposed solution is based on the assumption that the difference between the contract balance and the total balance of all participants in a smart contract before and after any operation, must be the same before and after any operation that changes the state of a contract. A proof-of-concept implementation shows that the proposed solution enables the detection and prevention of reentrancy attacks during the execution of smart contracts.

Author contributions

All authors listed have made a substantial, direct, and intellectual contribution to the work and approved it for publication.

Acknowledgments

We deeply thank all the authors and reviewers who have participated in this Research Topic.

Conflict of interest

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Publisher's note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Burns, J. (1978). Leadership . New York, NY: Harper and Row.

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Festinger, L. (1954). A theory of social comparison processes. Hum. Relat . 7, 117–140. doi: 10.1177/001872675400700202

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Valstar, M. F., Sanchez-Lozano, E., Cohn, J. F., Jeni, L. A., Gizard, J. M., Zhang, Z., et al. (2017). FERA: 2017-Addressing head pose in the third facial expression recognition and analysis challenge. Proc. Int. Conf. Autom. Face Gesture Recognit . 2017, 839–847. doi: 10.1109/FG.2017.107

PubMed Abstract | CrossRef Full Text | Google Scholar

Keywords: human behavior, technologies for the visually impaired, color representation, pattern formation, facial action coding, mobile sensors, gait recognition, phishing and reentrancy attacks

Citation: Lukowicz P, Nijholt A, Siddiqi K, Pelillo M, Laerhoven KV, Viganò L and Zannone N (2022) Editorial: 2021 editors' pick: Computer science. Front. Comput. Sci. 4:1062066. doi: 10.3389/fcomp.2022.1062066

Received: 05 October 2022; Accepted: 17 October 2022; Published: 07 November 2022.

Edited and reviewed by: Federica Paci , University of Verona, Italy

Copyright © 2022 Lukowicz, Nijholt, Siddiqi, Pelillo, Laerhoven, Viganò and Zannone. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.

*Correspondence: Kaleem Siddiqi, siddiqi@cim.mcgill.ca

This article is part of the Research Topic

2021 Editor's Pick: Computer Science

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Student spotlight: Victory Yinka-Banjo

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This interview is part of a series from the MIT Department of Electrical Engineering and Computer Science featuring students answering questions about themselves and life at the Institute. Today’s interviewee, Victory Yinka-Banjo, is a junior majoring in MIT Course 6-7: Computer Science and Molecular Biology. Yinka-Banjo keeps a packed schedule: She is a member of the Office of Minority Education (OME) Laureates and Leaders program ; a 2024 fellow in the public service-oriented BCAP program ; has previously served as secretary of the African Students’ Association, and is now undergraduate president of the MIT Biotech Group ; additionally, she is a SuperUROP Scholar ; a member of the Ginkgo Bioworks' Cultivate Fellowship (a program that supports students interested in synthetic biology/biotech); and an ambassador for Leadership Brainery , which equips juniors/leaders of color with the resources needed to prepare for graduate school. She recently found time to share a peek into her MIT experience.

Q: What’s your favorite building or room within MIT?

A: It has to be the Broad Institute of MIT and Harvard on Ames Street in Kendall Square, where I do my SuperUROP research in Caroline Uhler's lab . Outside of classes, you're 90 percent likely to find me on the newest mezzanine floor (between the 11th and 12th floor), in one of the UROP [Undergraduate Research Opportunities Program] rooms I share with two other undergrads in the lab. We have standing desks, an amazing coffee/hot chocolate machine, external personal monitors, comfortable sofas — everything, really! Not only is it my favorite building, it is also my favorite study spot on campus. In fact, I am there so often that when friends recently planned a birthday surprise for me, they told me they were considering having it at the Broad, since they could count on me being there.

I think the most beautiful thing about this building, apart from the beautiful view of Cambridge we get from being on one of the highest floors, is that when I was applying to MIT from high school, I had fantasized working at the Broad because of the groundbreaking research. To think that it is now a reality makes me appreciate every minute I spend on my floor, whether I am doing actual research or some last-minute studying for a midterm.

Q: Tell me about one interest or hobby you’ve discovered since you came to MIT.

A: I have become pretty involved in the performing arts since I got to MIT! I have acted in two plays run by the Black Theater Guild, which was revived during my freshman year by one of my friends. I played a supporting role in the first play called “Nkrumah’s Last Day,” which was about Ghana at a time of governance under Kwame Nkrumah, its first president. In the second play, a ghost story/comedy called “Shooting the Sheriff,” I played one of the lead roles. Both caused me to step way out of my comfort zone and I loved the experiences because of that. I also got to act with some of my close friends who were first-time stage actors as well, so that made it even more fun.

Outside of acting, I also do spoken word/poetry. I have performed at events like the African Students Association Cultural Night, MIT Africa Innovate Conference, and Black Women’s Alliance Banquet. I try to use my pieces to share my experiences both within and beyond MIT, offering the perspective of an international Nigerian student. My favorite piece was called “Code Switch,” and I used concepts from [computer science] and biology (especially genetic code switching), to draw parallels with linguistic code-switching, and emphasize the beauty and originality of authenticity. This semester, I’m also a part of MIT Monologues and will be performing a piece called “Inheritance,” about the beauty of self-love found in affection transferred from a mother.

Q: Are you a re-reader or a re-watcher — and if so, what are your comfort books, shows, or movies?

A: I don’t watch too many movies, although I used to be obsessed with all parts of “High School Musical;” and the only book I’ve ever reread is “Americanah.” I would actually say I am a re-podcaster! My go-to comfort-podcast is this episode, “A Breakthrough Unfolds”, by Google DeepMind . It makes me a little emotional every time I listen. It is such an exemplification of the power of science and its ability to break boundaries that humans formerly thought impossible. As a computer science and biology major, I am particularly interested in these two disciplines’ applications to relevant problems, like the protein-folding problem discussed in the episode, which DeepMind's solution for has caused massive advances in the biotech industry. It makes me so hopeful for the future of biology, and the ways in which computation can advance human health and precision medicine.

Q: Who’s your favorite artist?

A: When I think of the word 'artist,' I think of music artists first. There are so many who I love; my favorites also evolve over time. I’m Christian, so I listen to a lot of gospel music. I’m also Nigerian so I listen to a lot of Afrobeats. Since last summer, I’ve been obsessed with Limoblaze , who fuses both gospel and Afrobeats music! KB, a super talented gospel rapper , is also somewhat tied in ranking with Limo for me right now. His songs are probably ~50 percent of my workout playlist.

Q: It’s time to get on the shuttle to the first Mars colony, and you can only bring one personal item. What are you going to bring?

A: Oooh, this is a tough one, but it has to be my Brass Rat. Ever since I got mine at the end of sophomore year, it’s been nearly impossible for me to take it off. If there’s ever a time I forget to wear it, my finger feels off for the entire day.

Q: Tell me about one conversation that changed the trajectory of your life.

A: Two specific career-defining moments come to mind. They aren’t quite conversations, but they are talks/lectures that I was deeply inspired by. The first was towards the end of high school when I watched this TEDx Talk about storing data in DNA . At the time, I was getting ready to apply to colleges and I knew that biology and computer science were two things I really liked, but I didn’t really understand the possibilities that could be birthed from them coming together as an interdisciplinary field. The TEDx talk was my eureka moment for computational biology.

The second moment was in my junior fall during an introductory lecture to “Lab Fundamentals for Bioengineering,” by Professor Jacquin Niles. I started the school year with a lot of confusion about my future post-grad, and the relevance of my planned career path to the communities that I care about. Basically, I was unsure about how computational biology fit into the context of Nigeria’s problems, especially because my interest in the field is oriented towards molecular biology/medicine, not necessarily public health.

In the U.S., most research focuses on diseases like cancer and Alzheimer’s, which, while important, are not the most pressing health conditions in tropical regions like Nigeria. When Professor Niles told us about his lab’s dedication to malaria research from a molecular biology standpoint, it was yet another eureka moment. Like, Yes! Computation and molecular biology can indeed mitigate diseases that affect developing nations like Nigeria — diseases that are understudied, and whose research is underfunded.

Since his talk, I found a renewed sense of purpose. Grad school isn’t the end goal. Using my skills to shine a light on the issues affecting my people that deserve far more attention is the goal. I’m so excited to see how I will use computational biology to possibly create the next cure to a commonly neglected tropical disease, or accelerate the diagnosis of one. Whatever it may be, I know that it will be close to home, eventually.

Q: What are you looking forward to about life after graduation? What do you think you’ll miss about MIT?

A: Thinking about graduating actually makes me sad. I’ve grown to love MIT. The biggest thing I’ll miss, though, is Independent Activities Period (IAP). It is such a unique part of the MIT experience. I’ve done a web development class/competition, research, a data science challenge, a molecular bio crash course, and a deep learning crash course over the past three IAPs. It is such an amazing time to try something low stakes, forget about grades, explore Boston, build a robot, travel abroad, do less, go slower, really rejuvenate before the spring, and embrace MIT’s motto of “mind and hand” by just being creative and explorative. It is such an exemplification of what it means to go here, and I can’t imagine it being the same anywhere else.

That said, I look forward to graduating so I can do more research. My hours spent at the Broad thinking about my UROP are always the quickest hours of my week. I love the rabbit holes my research allows me to explore, and I hope that I find those over and over again as I apply and hopefully get into PhD programs. I look forward to exploring a new city after I graduate, too. I wouldn’t mind staying in Cambridge/Boston. I love it here. But I would welcome a chance to be somewhere new and embrace all the people and unique experiences it has to offer.

I also hope to work on more passion projects post-grad. I feel like I have this idea in my head that once I graduate from MIT, I’ll have so much more time on my hands (we’ll see how that goes). I hope that I can use that time to work on education projects in Nigeria, which is a space I care a lot about. Generally, I want to make service more integrated in my lifestyle. I hope that post-graduation, I can prioritize doing that even more: making it a norm to lift others as I continue to climb.

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Reflecting on a decade of SuperUROP at MIT

The MIT African Students Association recently celebrated its 21st annual cultural night, themed Kijiji — Swahili for “village.”

A community for African students at MIT

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Playboy image from 1972 gets ban from ieee computer journals, use of "lenna" image in computer image processing research stretches back to the 1970s..

Benj Edwards - Mar 29, 2024 9:16 pm UTC

On Wednesday, the IEEE Computer Society announced to members that, after April 1, it would no longer accept papers that include a frequently used image of a 1972 Playboy model named Lena Forsén. The so-called " Lenna image ," (Forsén added an extra "n" to her name in her Playboy appearance to aid pronunciation) has been used in image processing research since 1973 and has attracted criticism for making some women feel unwelcome in the field.

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