research topics for computer engineering

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

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

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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Investigating the impacts of software refactoring techniques and tools in blockchain-based developments.

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Computing Engineering Dissertation Topics

Published by Jamie Walker at January 10th, 2023 , Revised On August 18, 2023

Over a period of time, dissertations have become an inherent component of higher education studies. They are not only entrenched within the master or a PhD. Degree but also in undergraduate programmes. Computer engineering dissertations allow the researchers to choose a topic of particular interest to them and research further into the topic to add to the current body of literature.

However, choosing a topic from an extensive list of topics is always easier than working on the first topic you find interesting.

To help you get started with brainstorming for computer topic ideas, we have developed a list of the latest computer engineering dissertation topics that can be used for writing your computer engineering dissertation.

These topics have been developed by PhD-qualified writers of our team , so you can trust to use these topics for drafting your dissertation.

You may also want to start your dissertation by requesting  a brief research proposal  from our writers on any of these topics, which includes an  introduction  to the problem,  research questions , aim and objectives ,  literature review  along with the proposed  methodology  of research to be conducted.  Let us know  if you need any help in getting started.

Check our  example dissertations  to get an idea of  how to structure your dissertation .

You can review step by step guide on how to write your dissertation  here.

View our free dissertation topics database.

Computer Engineering Dissertation Topics

Computers are the greatest innovation of the modern era and have done wonders for mankind. There is only one language that computers understand; the binary. But there are various high-level coding languages that even computers do not understand and therefore use a compiler for translation.

Computing refers to computer hardware or software coding development technology and covers all aspects of computer technologies. It is the practical and scientific study of the implementation of computing information. Computing is also interchangeably known as computer sciences.

A computing engineer or a computer scientist specializes in practical work, the theory of computing, and the design of computational systems. Essentially, it is the study of structure, expression, mechanization, and feasibility of algorithms (logical procedures) that cause processing, communication, representation, access of information, and acquisition in a computer. This area has a wide range of topics, some of which have been listed below:

• Risk calculation in the application and development process.
• Generation of the java application.
• Implementing a behavioural based approach to detect cheating in online games.
• Analysis of coding environment of different applications.
• Identification of different languages used for coding.
• Identification of stake holder’s interest in App development process.
• Role of visualization in complex hierarchal structures of computing.
• Analysing the requirements of Inventory Management Software.
• Development of single-player simulation game.
• Investigation of web teaching aid system.
• Development of online based library management system.
• Implementation of Electronic banking system.

2022 Computing Engineering Dissertation Topics

Topic 1: an investigation of the blockchain's application on the energy sector leading towards electricity production and e-mobility..

Research Aim: This study aims to investigate the applications of blockchain within the energy sector. This study will identify how blockchain can be used to produce electricity from the comfort of home. Moreover, this study aims to introduce the concept of e-mobility through blockchain, according to which blockchain can be used to share the car ride with the other commuters residing at nearby places. Another objective of this research is to develop a framework that could assess blockchain’s use for the consumers staying within a budget and letting them assess how much money they have been spending so far.

Topic 2: Investigating the Issues that Impact Data Security in Cloud-Based Blockchain Technology: An Global Tourism Industry Case Study

Research Aim: This research focuses on a significant shift in trend found in the worldwide tourist business, which is the usage of the cloud for data and services. It attempts to supply the requirements for this implementation owing to the demand for ease, saving, and improved service providing. Furthermore, it will also focus on the limits of traditional blockchain technology primitives and assess control models. These constraints are related to security issues involving data in a cloud environment in the global tourism industry.

Topic 3: Is Digital Technology overtaking Human Interactions in the Medical Fields? An Examination of the Use of Computational biology and Machine Leaning in Patient Diagnosis and Treatment.

Research Aim: The current study seeks to examine how digital technology is replacing human interactions in the medical industry in the importance of computational biology and machine learning in patient diagnosis and treatment. This study will set forth the theoretical foundations and significance of computational biology and machine learning and will also make recommendations for further enhancement.

Topic 4: Evaluating the use of databases and information retrieval systems in the perspective of the United States National security policy.

Research Aim: The current study aims to evaluate the use of databases and information retrieval systems from the perspective of United States national security policy. This study addresses the databases and information retrieval system to provide a clear understanding. It will also focus on specific elements d criteria in the united state’s national security and highlights the benefits and drawbacks of employing them to enhance national security strategy in the united states.

Topic 5: Development of growing infusion of computer technology in the area of medicine- examining NHS policies.

Research Aim: This research aims to examine the development of the growing infusion of computer technology in the area of medicine by evaluating existing NHS policy. The study will provide a theoretical framework for the application of computer science technologies in medicine and will also set out the benefits of using contemporary computer technology as well as analyse the drawbacks that have occurred as a result of the growth of this new technology in this field. It will also focus on the policies employed by NHS to assist the development of technologies in the UK healthcare sector.

Computer Engineering Dissertation Topics for 2021

A 3-d visualization system for ultrasound images.

Research Aim: This research will focus on the visualization of 3-D ultrasound images and their medical therapy benefits.

Reliable and realistic study of remote communication systems in telephony and multipath faded systems

Research Aim: This research’s primary emphasis is on telephony’s practical implementation in a remote communication system.

Establishing a Neural Network Device

Research Aim: In terms of energy efficiency, the human brain is much greater than any modern supercomputer. A whole new generation of energy-efficient, brain-like computers is being designed for this study.

Methods for Artifact EEG Brain function study, caused by sugar, salt, fat, and their replacements

Research Aim: This study relies on the procedure for calculating sweetness taste is developed and validated. Part of the project includes modern electrode technologies to capture the purest possible brain signal from EEG equipment.

Find 100s of dissertation topics in your other academic subjects in our free topics database.

The impact of Covid-19 on tech spends in 2021

Research Aim: This research aims to study the impact of Covid-19 on tech spends in 2021.

Analysis of information system built for e-learning

Research Aim: This research aims to analyze the information system built for e-learning

Advantages and disadvantages of an information system

Research Aim: This research aims to address the advantages and disadvantages of an information system.

Covid-19 Computer Engineering Research Topics

Research to study the effects of coronavirus on it industries.

Research Aim: This research will focus on the impacts of COVID-19 on the growth of IT industries highlighting the issues responsible for it and the possible solutions to overcome them.

Research to identify the impact of Coronavirus on the computer science research community

Research Aim: Coronavirus has infected thousands of people and has been responsible for the deaths of several innocent people worldwide. This study will focus on identifying the effects of this pandemic on the computer science research community.

Research to study the impacts of COVID-19 on tech spends in 2021

Research Aim: As a result of COVID-19, the economy of the entire world has been disrupted. The purpose of this research is to know the tech expenditures after COVID-19 became widespread. How are the tech industries dealing with the challenging situation created by COVID-19?

Research to identify the contribution of computer science to control the spread of Coronavirus pandemic

Research Aim: This research aims at identifying the contributions and efforts made by computer engineers to control the pandemic. What is the role of computer scientists during the pandemic?

Research to identify the unemployment of computer engineers after the Coronavirus pandemic

Research Aim: This research will focus on identifying the increased unemployment issues raised after the COVID-19 pandemic and finding out the possible solutions to overcome the reduced unemployment of computer engineers.

Hardware, Network and Security Dissertation Topics

Network security is very crucial for any organisation. It is dependent upon a well-managed network through the implementation of policies drafted by network administrators to manage the access of the organisational information. Network security provides stability, safety, integrity, reliability, and utility of data and network.

It works efficiently with the latest hardware equipment and updated software. Network security offers many advantages to businesses, such as protection against any disruption to keep employees motivated, energetic, regular, and productive.

In certain instances, a virus may break into the network security. However, the network administrator generally uses an anti-virus program to prevent this sort of attack.

Therefore, it will be fair to say that network security plays a vital role in maintaining a business’s reputation and operations which is the most important asset to any organisation. Below is a list of topics that you can base your dissertation on:

• Performance analysis of transmission control protocol over Ethernet LAN.
• Gateway usage for the intrusion detection system.
• Impact of security machinimas in online transactions.
• Investigation of smart card specification.
• Importance of router placement in the network.
• Level of customer’s trust in E-banking.
• Role of antivirus in a shared network.
• Application of database technologies for data network management.
• Network worm: A headache to networking.
• Implementation of various tools in programming language.
• Study of retroactive data structures.
• Role of Voice over Internet Protocol over Ethernet LAN.
• The usefulness of data transfer security over Wi-Fi Network.
• Influence of signal strength of Wi-Fi upon data transfer.
• Analysis of tree inclusion complexities.
• Analysis of the implementation of the set procedure.
• Analysis of the application of programming tools.
• Implementation of File Sharing System in Network.
• Study of virus behaviours in the secured programming environment.
• Investigation of issues of user’s security and data protection over the network.
• Benefits of network security to customers.
• Improvements of mobile data service for future usage.
• Study of Asymmetry k-center variant.
• Analysis of issues in emerging 4G networks.
• Role of dynamic proxies in a mobile environment to support Remote method Invocation.

Software, Programming and Algorithm Dissertation Topics

In layman language, the software is collectively known as the “combination of operating information and all the programs that are being used by the computer.” It is a set of instructions to direct computers to perform a specific task depending upon thususer’s instructions.

The software can be written in both high and low-level languages. Low-level language is also known as machine code and is faster because it doesn’t require any compiler and directly communicates with the computer. A high-level language is pretty similar to a human language, and therefore can be easily understood by the developers. High-level language requires the compiler to translate commands to the computer.

Programming and algorithms can be termed as commands given to the computer to perform actions. Programming leads to executable programs from a computing problem and involves developing, generating, and analysing algorithms. Algorithms refer to an act done involving a step-by-step process to solve a problem. It is a set of logic written in software.

There are two types of software; operating software that helps in operation and system software necessary to run a system. Operating software can be rewritten and changed according to demand, but system software cannot be altered. If developers require any alterations, they would have to develop new software.

There are various topics that can be considered for  research dissertation purposes  under this theme, a list of which is given below.

• Application of algorithms.
• Importance of approximation algorithms on graphs.
• Critical analysis of data structures on trees.
• Evaluation and implementation of new algorithms.
• System software: A link to communicate hardware.
• Difference between binary dispatching and multiple dispatching.
• Analysis of plan sweep techniques.
• Investigation of software support to drivers of devices.
• Intelligent interface for database systems.
• Analysis of function and types of union-find.
• The usefulness of different coding languages.
• Application of basic hardware knowledge and math skills.
• Analysis of the design of converter based on new moduli.
• Analysis of information travelling via software.
• Evaluation and implementation of heuristic algorithms.
• Development of applications using Java.
• Analysis of fault tolerance in a network by using simulation.
• Importance of system software for computers.
• Effects of larger integer module operations.
• Consequences of wrong commands in coding.
• Investigation of the coding language of system software.
• Analysis of feasibility environment of platform.
• Evaluation of heuristic algorithms for generating clusters.
• Critical analysis of fixed control variable.
• Analysis of design of converter with large dynamic range.
• Ways to recover corrupted software.
• Analysis of fault tolerance of sorting network.
• Analysis of the difference between LAN and WAN.
• Development of an algorithm for a one-way hashing system.
• Relation between dynamic access and fixed values.
• Importance of right language selection while coding.
• Study of optimization problems.
• Analysis of security frameworks for web services.
• Investigating algorithms techniques.
• Partial persistence of algorithms vs others.
• Study of time and space problems of algorithmic functions.
• Effects of linear and logarithmic factors over programming.
• Discussion about union-find with deletion.
• Importance of data structure for bridge core problems.
• Consequences of fault in interconnected networks.
• Difference between rooted and unrooted tree.

Information Systems Dissertation Topics

Information systems refer to a group of people and computers that are being used for the interpretation of all kinds of information. Computer-based information systems are a very interesting topic for research. It includes all information regarding decision making, management support, and operations and can also be used to access the database.

There is an obvious difference between computer systems, information systems, and business processes. The information system provides the tools to manage businesses successfully.

An information system can be said to be a workstation where humans and machines work together towards the success of a business. One such example is Wal-Mart. The company is entirely based on information systems and has connected its suppliers, vendors, customers and together.

It deals with a large number of data and consists of hardware, software, network, and telecommunications of the operation. Below is a list of research topics in the field of information systems for you to base your dissertation  on:

• Analysis of challenges in building information systems for any organisation.
• Impact of cyberinfrastructure on the customer.
• Role of information system in scientific innovations.
• The usefulness of information systems for businesses.
• Advantages of information systems.
• Access to information systems by employees anywhere in the world.
• Preparation of a database management system.
• Analysis and solution of database management systems.
• Study of support of information system to hardware.
• Managing information systems of big stores, The case of Walmart.
• Analysis of information system built for E-learning.
• Critical analysis of the changing nature of the web.
• Role of information system in decision making of disruptions.
• Examine customer response through the information system.
• Investigate the impact of a virus in the network
• Relationship between I.T education and an organization.
• Role of information system in global warming.
• Investigate the reason for adopting green information systems.
• Analysis of the between social networks and information systems.
• Role of information system in dealing with complex business problems.

Important Notes:

As a computing engineering student looking to get good grades, it is essential to develop new ideas and experiment with existing computing engineering theories – i.e., to add value and interest in your research topic.

The field of computing engineering is vast and interrelated to so many other academic disciplines like civil engineering , finance , construction ,  law ,  healthcare , mental health , artificial intelligence , tourism , physiotherapy , sociology , management , marketing and nursing . That is why it is imperative to create a project management dissertation topic that is articular, sound, and actually solves a practical problem that may be rampant in the field.

We can’t stress how important it is to develop a logical research topic; it is the basis of your entire research. There are several significant downfalls to getting your topic wrong; your supervisor may not be interested in working on it, the topic has no academic creditability, the research may not make logical sense, there is a possibility that the study is not viable.

This impacts your time and efforts in  writing your dissertation  as you may end up in the cycle of rejection at the very initial stage of the dissertation. That is why we recommend reviewing existing research to develop a topic, taking advice from your supervisor, and even asking for help in this particular stage of your dissertation.

While developing a research topic, keeping our advice in mind will allow you to pick one of the best computing engineering dissertation topics that fulfill your requirement of writing a research paper and add to the body of knowledge.

Therefore, it is recommended that when finalizing your dissertation topic, you read recently published literature to identify gaps in the research that you may help fill.

Remember- dissertation topics need to be unique, solve an identified problem, be logical, and be practically implemented. Take a look at some of our sample computing engineering dissertation topics to get an idea for your own dissertation.

How to Structure your Dissertation on Computing Engineering

A well-structured   dissertation can help students   to achieve a high overall academic grade.

• A Title Page
• Acknowledgements
• Declaration
• Abstract: A summary of the research completed
• Table of Contents
• Introduction : This chapter includes the project rationale, research background, key research aims and objectives, and the research problems to be addressed. An outline of the structure of a dissertation  can also be added to this chapter.
• Literature Review :  This chapter presents relevant theories and frameworks by analysing published and unpublished literature available on the chosen research topic, in light of  research questions  to be addressed. The purpose is to highlight and discuss the relative weaknesses and strengths of the selected research area whilst identifying any research gaps. Break down of the topic, and key terms can have a positive impact on your dissertation and your tutor.
• Methodology:  The  data collection  and  analysis  methods and techniques employed by the researcher are presented in the Methodology chapter which usually includes  research design,  research philosophy, research limitations, code of conduct, ethical consideration, data collection methods and  data analysis strategy .
• Findings and Analysis:  Findings of the research are analysed in detail under the Findings and Analysis chapter. All key findings/results are outlined in this chapter without interpreting the data or drawing any conclusions. It can be useful to include  graphs , charts, and   tables in this chapter to identify meaningful trends and relationships.
• Discussion  and  Conclusion: The researcher presents his interpretation of results in this chapter, and states whether the research hypothesis has been verified or not. An essential aspect of this section of the paper is to draw a linkage between the results and evidence from the literature. Recommendations with regards to implications of the findings and directions for the future may also be provided. Finally, a summary of the overall research, along with final judgments, opinions, and comments, must be included in the form of suggestions for improvement.
• References:  This should be completed in accordance with your University’s requirements
• Bibliography
• Appendices:  Any additional information, diagrams, graphs that were used to  complete the  dissertation  but not part of the dissertation should be included in the Appendices chapter. Essentially, the purpose is to expand the information/data.

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How to find dissertation topics about computing engineering.

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• Explore emerging technologies.
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• Select a topic aligning with your passion and career aspirations.

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Available for single-semester IW and senior thesis advising, 2024-2025

• 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

Available for Fall 2024 IW advising, only

• 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

Not available for IW or thesis advising, 2024-2025

• 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

• 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

• 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

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

Jia Deng, Room 423

•  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

• 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

• 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

• 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

Available for Fall 2024 single-semester IW advising, only

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

Peter Henderson , 302 Sherrerd Hall

• Research Areas: Machine learning, law, and policy

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.

• 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.

Pravesh Kothari, Room 320

• Research areas: Theory

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.

Lydia Liu, Room 414

• Research Areas: algorithmic decision making, machine learning and society
• Theoretical foundations for algorithmic decision making (e.g. mathematical modeling of data-driven decision processes, societal level dynamics)
• Societal impacts of algorithms and AI through a socio-technical lens (e.g. normative implications of worst case ML metrics, prediction and model arbitrariness)
• Machine learning for social impact domains, especially education (e.g. responsible development and use of LLMs for education equity and access)
• Evaluation of human-AI decision making using statistical methods (e.g. causal inference of long term impact)

Wyatt Lloyd, Room 323

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

Alex Lombardi , Room 312

• Research Areas: Theory

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 

Available for Spring 2025 single-semester IW, only

• 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.

Mae Milano, Room 307

• Local-first / peer-to-peer systems
• Wide-ares storage systems
• Consistency and protocol design
• Type-safe concurrency
• Language design
• Gradual typing
• Domain-specific languages
• Languages for distributed systems

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

• 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

Available for Fall 2024 single-semester IW, only

• 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

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

• Privacy Policy

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Home » 500+ Computer Science Research Topics

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.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Areas of Research in CSE

A unique and robust research environment

Artificial Intelligence

Experimental and applied investigations of intelligent systems, including rational decision making, machine learning and perception, natural language processing, cognitive modeling, and more.

Learn more >

Chip Design, Architecture, & Emerging Devices

Silicon chip design, computer architecture, and novel device technologies that may replace traditional CMOS transistors.

Databases & Data Mining

Building the data management infrastructure for the twenty-first century, with particular emphasis on issues surrounding Big Data.

Embedded & Mobile Systems

Designing systems for vehicles, wireless sensors, medical devices, wearable fitness devices, smartphones, and other devices not generally considered to be computers.

Formal Methods & Automated Reasoning

Developing and deploying mathematically-rigorous and algorithmically-efficient solutions to prove the correct behavior of complex hardware and software.

Human-Computer Interaction

Investigating exciting new services in educational technology, multimedia, and social computing, as well as the domains of human perception and cognition, social activity, and learning.

Languages, Compilers, & Runtime Systems

Static and run-time compiler systems are used to get more performance, robustness, and energy efficiency with program analysis, transformation, and adaptation.

Networking, Operating Systems, & Distributed Systems

Research from wireless networking and mobile computing to the Internet and datacenter networks, and tackling exciting new problems that span embedded systems, sensor networks, data centers, and cloud services.

We use artificial intelligence techniques for dealing with planning and uncertainty, localization and mapping, sensor processing and classification, and continuous learning.

Secure, Trustworthy, & Reliable Systems

Addressing key security challenges through near-term stress reduction techniques to improve the quality of silicon and longer-term technologies to detect, recover, and repair faulty systems.

Theory of Computation

Conducting research across many areas such as data structures, cryptography, quantum computing, parallel and distributed computing, algorithmic game theory, graph theory, geometry, combinatorics, and energy efficiency.

Warehouse-Scale & Parallel Systems

Pursuing the design of the hardware and software infrastructure for massive-scale computing systems. Major research topics include server architecture, GPU computing, emerging memory technologies, distributed software, and more.

• M.Eng. Admissions and Applying
• Design Project and Annual Poster Session
• M.Eng. Degree Requirements
• Post-Grad Activities/Career Information
• Ph.D. Program
• Undergraduate Programs
• Schedule a tour

Strategic Research Areas

• Research Groups, Centers and Labs
• Undergraduate Research Opportunities
• Executive Leadership
• Administrative Staff
• Faculty Awards and Honors
• Resources and Groups for ECE Women
• ECE Advisory Council
• ECE Connections
• Giving Opportunities
• Ways to Give
• Academic Support
• Financial Support
• Mental Health Resources
• Experience and Employment
• Undergraduate Services
• Graduate Services and Activities

Research in Electrical and Computer Engineering covers an extremely broad range of topics. Whether in computer architecture, energy and power systems or in nanotechnology devices, the research conducted in ECE is at the cutting edge of technological and scientific developments. 

• Computer Engineering

Computer engineering concerns itself with the understanding and design of hardware needed to carry out computation, as well as the hardware-software interface. It is sometimes said that computer engineering is the nexus that connects electrical engineering and computer science. Research and teaching areas with a significant computer engineering component include digital logic and VLSI design, computer architecture and organization, embedded systems and Internet of things, virtualization and operating systems, code generation and optimization, computer networks and data centers, electronic design automation, or robotics.

Related Research Areas

• Artificial Intelligence
• Complex Systems, Network Science and Computation
• Computer Architecture
• Computer Systems
• Data Mining
• Energy and the Environment
• Rapid Prototyping

Robotics and Autonomy

• Scientific Computing
• Sensors and Actuators
• Signal and Image Processing
• Statistics and Machine Learning

Robotics at Cornell spans various subareas, including perception, control, learning, planning, and human-robot interaction. We work with a variety of robots such as aerial robots, home and office assistant robots, autonomous cars, humanoids, evolutionary robots, legged robots, snake robots and more. The Collective Embodied Intelligence Lab  works to design and coordination of large robot collectives able to achieve complex behaviors beyond the reach of single robot systems, and corresponding studies on how social insects do so in nature. Major research topics include swarm intelligence, embodied intelligence, autonomous construction, bio-cyber physical systems, human-swarm interaction, and soft robots.

Visit the the  Cornell Engineering Robotics Website  for more.

• Integrated Circuits
• Power Electronics
• Robotics and Autonomy
• Systems and Networking

• Information, Networks, and Decision Systems

This research area focuses on the advancement of research and education in the information, learning, network, and decision sciences. Our research is at the frontier of a wide range of fields and applications, including machine learning and signal processing, optimization and control theory, information theory and coding, power systems and electricity markets, network science, and game theory. The work encompasses theory and practice, with the overarching objective of developing the mathematical underpinnings and tools needed to address some of the most pressing challenges facing society today in energy and climate change, transportation, social networks, and human health. In particular, the Foundations of Information, Networks, and Decision Systems (FIND) group comprises a vibrant community of faculty, postdocs, and students dedicated to developing the mathematical underpinnings and tools needed to address the aforementioned challenges in a principled and theory-guided manner.

• Biotechnology
• Computational Science and Engineering
• Energy Systems
• Image Analysis
• Information Theory and Communications
• Optimization
• Remote Sensing

• Physical Electronics, Devices, and Plasma Science

Work in this area applies the physics of electromagnetism, quantum mechanics, and the solid state to implement devices and systems for applications including energy, quantum technologies, sensing, communication, and computation. Our efforts span theory and development of new electronic and optical devices and materials, micro-electromechanical systems, acoustic and optical sensing and imaging, quantum control of individual atoms near absolute zero temperature, and experiments on high-energy plasmas at temperatures close to those at the center of the sun.    At Cornell ECE, we work on diverse topics aimed at transforming the way we view the world. Our interdisciplinary research reveals fundamental similarities across problems and prompts new research into some of the most exciting and cutting-edge developments in the field.

• Advanced Materials Processing
• Astrophysics, Fusion and Plasma Physics
• High Energy Density, Plasma Physics and Electromagnetics
• Materials Synthesis and Processing
• Microfluidics and Microsystems
• Nanotechnology
• Photonics and Optoelectronics
• Semiconductor Physics and Devices
• Solid State, Electronics, Optoelectronics and MEMs

• Circuits and Electronic Systems

Integrated circuits are ubiquitous and integral to everyday devices, from cellular phones and home appliances to automobiles and satellites. Healthcare, communications, consumer electronics, high-performance scientific computing, and many other fields are creating tremendous new opportunities for innovation in circuits and electronic systems at every level. Research in this area spans topics including analog and mixed signal circuits, RF transceivers, low power interfaces, power electronics and wireless power transfer, and many others. 

• Micro Nano Systems
• Optical Physics and Quantum Information Science

• Bio-Electrical Engineering

Biological and Biomedical Electrical Engineering (B2E2) consists of both applied and fundamental work to understand the complexity of biological systems at different scales, e.g., from a single neuronal or cancer cell, all the way to the brain or malignant tumor. B2E2 aims to develop new hardware and computational tools to identify, characterize, and treat diseases. In the physical domain, electrical engineering approaches to integrated microsystems lead to new biological and medical sensors. These sensors consist of state-of-the-art ultrasonic, RF, optical, MRI, CT, electrical impedance transducers. 

The integration of sensors, electronics are used to develop implantable and wearable devices, with decreasing size, weight, and power and increased functionality. B2E2 microsystems can help create interfaces for sensing and actuation to help understand the physiological and pathological mechanisms of diseases, and enable advanced robotic interfaces in medicine. Medical devices can generate vast amounts of data, which require both real-time and post-acquisition processing. B2E2 faculty, sometimes in collaboration with medical researchers, develop advanced computational tools to learn from and exploit data and apply artificial intelligence approaches to impact medical practice by improving: early disease detection, disease diagnosis, response to therapy assessment, and guided surgical procedures.

• Biomedical Imaging and Instrumentation
• Complex Systems, Network Science and Technology
• Computer-Aided Diagnosis
• Nanobio Applications
• Neuroscience

Hardware That Protects Against Software Attacks

ECE's Ed Suh and Zhiru Zhang and CS's Andrew C. Myers aim to develop both hardware architecture and design tools to provide comprehensive and provable security assurance for future computing systems against software-level attacks that exploit seven common vulnerability classes.

Image credit Beatrice Jin

Re-architecting Next-Gen Computing Systems

Disaggregated architectures have the potential to increase resource capacity by 10 to 100 times server-centric architectures.

Re-imagining Computer System Memories

Interdisciplinary team will provide new insights and an entirely new paradigm for the semiconductor industry in the emerging era of big data.

Engineers to hack 50-year-old computing problem with new center

Cornell engineers are part of a national effort to reinvent computing by developing new solutions to the “von Neumann bottleneck,” a feature-turned-problem that is almost as old as the modern computer itself.

The Laboratory of Plasma Studies: Uncovering mysteries of high energy density plasma physics

In the basement of Grumman Hall, an x-ray pulse produced by a hot, dense plasma – an ionized gas – lasting only fractions of a microsecond both begins and ends an experiment. Hidden within that fraction of time lies a piece of a puzzle—data that graduate students and staff scientists at the Laboratory of Plasma Studies (LPS) will use to better understand the mysterious physics behind inertial confinement fusion.

Sophia Rocco: Hoping to make the world a better place through a potential renewable energy source

When she was looking at graduate schools, physics major Sophia Rocco thought she would be in a materials science program bridging her interests in electricity and magnetism and novel materials for solar cells. Chancing upon the School of Electrical and Computer Engineering at Cornell, she discovered the Laboratory of Plasma Studies (LPS).

Finding the Ultimate Energy Source: Cornell’s Lab of Plasma Studies

Plasma is one of the four fundamental states of matter, but it does not exist freely on the Earth’s surface. It must be artificially generated by heating or subjecting a neutral gas to a strong electromagnetic field. Located in the basement of Grumman Hall are two large pulse-power generators that create plasma by delivering extremely high currents to ordinary matter for short periods. These generators are part of the  Lab of Plasma Studies  at Cornell University.

Photo credit: Dave Burbank

Vertical gallium oxide transistor high in power, efficiency

The research group led by Grace Xing and Debdeep Jena presented research on a new gallium oxide field-effect transistor at a conference at the Massachusetts Institute of Technology May 29-June 1.

Molnar, Jena and Xing join national consortium to develop future cellular infrastructure

Three Cornell faculty will be part of the newly established $27.5 million ComSenTer, a center for converged terahertz communications and sensing.

Data on the Brain

The NSF has found a willing partner at Cornell University in this quest to create technologies that will allow researchers to image the brain and the nervous system.

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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.

Latest Research and Reviews

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News and Comment

AI now beats humans at basic tasks — new benchmarks are needed, says major report

Stanford University’s 2024 AI Index charts the meteoric rise of artificial-intelligence tools.

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Medical artificial intelligence should do no harm

Bias and distrust in medicine have been perpetuated by the misuse of medical equations, algorithms and devices. Artificial intelligence (AI) can exacerbate these problems. However, AI also has potential to detect, mitigate and remedy the harmful effects of bias to build trust and improve healthcare for everyone.

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Generative artificial intelligence in chemical engineering

Generative artificial intelligence will transform the way we design and operate chemical processes, argues Artur M. Schweidtmann.

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Why scientists trust AI too much — and what to do about it

Some researchers see superhuman qualities in artificial intelligence. All scientists need to be alert to the risks this creates.

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Computer science deals with the theory and practice of algorithms, from idealized mathematical procedures to the computer systems deployed by major tech companies to answer billions of user requests per day.

Primary subareas of this field include: theory, which uses rigorous math to test algorithms’ applicability to certain problems; systems, which develops the underlying hardware and software upon which applications can be implemented; and human-computer interaction, which studies how to make computer systems more effectively meet the needs of real people. The products of all three subareas are applied across science, engineering, medicine, and the social sciences. Computer science drives interdisciplinary collaboration both across MIT and beyond, helping users address the critical societal problems of our era, including opportunity access, climate change, disease, inequality and polarization.

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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 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.

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.

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.

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.

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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Optimizing nuclear fuels for next-generation reactors

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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.

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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.

Top 10 Computer Science Research Paper Topics

1. battery life and energy storage for 5g equipment.

The 5G network is an upcoming cellular network with much higher data rates and capacity than the current 4G network. According to research published in the European Scientific Institute Journal, one of the main concerns with the 5G network is the high energy consumption of the 5G-enabled devices . Hence, this research on this topic can highlight the challenges and proffer unique solutions to make more energy-efficient designs.

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.

Other Examples of Computer Science Research Topics & Questions

Computer research topics.

• The confluence of theoretical computer science, deep learning, computational algorithms, and performance computing
• Exploring human-computer interactions and the importance of usability in operating systems
• Predicting the limits of networking and distributed systems
• Controlling data mining on public systems through third-party applications
• The impact of green computing on the environment and computational science

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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Research Opportunities

Undergraduate research in computer science.

For specific information on undergraduate research opportunities in Computer Science visit  https://csadvising.seas.harvard.edu/research/ .

General Information about Undergraduate Research

Opportunities for undergraduates to conduct research in engineering, the applied sciences, and in related fields abound at Harvard. As part of your coursework, or perhaps as part of individual research opportunities working with professors, you will have the chance to  take part in or participate in  some extraordinary projects covering topics ranging from bioengineering to cryptography to environmental engineering.

Our dedicated undergraduate research facilities and Active Learning Labs also provide opportunities for students to engage in hands-on learning. We encourage undergraduates from all relevant concentrations to tackle projects during the academic year and/or over the summer.

Keep in mind, many students also pursue summer research at private companies and labs as well as at government institutions like the National Institutes of Health.

If you have any questions, please contact or stop by the Office of Academic Programs, located in the Science and Engineering Complex, Room 1.101, in Allston.

Research FAQs

The SEAS website has a wealth of information on the variety of cross-disciplinary research taking place at SEAS. You can view the concentrations available at SEAS here , as well as the research areas that faculty in these concentrations participate in. Note that many research areas span multiple disciplines; participating in undergraduate research is an excellent way to expand what you learn beyond the content of the courses in your concentration! 

To view which specific faculty conduct research in each area, check out the All Research Areas section of the website. You can also find a helpful visualization tool to show you the research interests of all the faculty at SEAS, or you can filter the faculty directory by specific research interests. Many faculty’s directory entry will have a link to their lab’s website, where you can explore the various research projects going on in their lab.

The Centers & Initiatives page shows the many Harvard research centers that SEAS faculty are members of (some based at SEAS, some based in other departments at Harvard). 

Beyond the website, there are plenty of research seminars and colloquia happening all year long that you can attend to help you figure out what exactly you are interested in. Keep an eye on the calendar at https://events.seas.harvard.edu ! 

There are several events that are designed specifically for helping undergraduate students get involved with research at SEAS, such as the Undergraduate Research Open House and Research Lightning Talks . This event runs every fall in early November and is a great opportunity to talk to representatives from research labs all over SEAS. You can find recordings from last year’s Open House on the SEAS Undergraduate Research Canvas site .

Most of our faculty have indicated that curiosity, professionalism, commitment and an open mind are paramount. Good communication skills, in particular those that align with being professional are critical. These skills include communicating early with your mentor if you are going to be late to or miss a meeting, or reaching out for help if you are struggling to figure something out. Good writing skills and math (calculus in particular) are usually helpful, and if you have programming experience that may be a plus for many groups. So try to take your math and programming courses early (first year) including at least one introductory concentration class, as those would also add to your repertoire of useful skills.

Adapted from the Life Sciences Research FAQs

Start by introducing yourself and the purpose of your inquiry (e.g. you’d like to speak about summer research opportunities in their lab). Next, mention specific aspects of their research and state why they interest you (this requires some background research on your part). Your introduction will be stronger if you convey not only some knowledge of the lab’s scientific goals, but also a genuine interest in their research area and technical approaches.

In the next paragraph tell them about yourself, what your goals are and why you want to do research with their group. Describe previous research experience (if you have any). Previous experience is, of course, not required for joining many research groups, but it can be helpful. Many undergraduates have not had much if any previous experience; professors are looking for students who are highly motivated to learn, curious and dependable.

Finally, give a timeline of your expected start date, how many hours per week you can devote during the academic term, as well as your summer plans.

Most faculty will respond to your email if it is clear that you are genuinely interested in their research and have not simply sent out a generic email. If you don’t receive a response within 7-10 days, don’t be afraid to follow up with another email. Faculty are often busy and receive a lot of emails, so be patient.

There are several ways that undergraduate research can be funded at SEAS. The Program for Research in Science and Engineering ( PRISE ) is a 10-week summer program that provides housing in addition to a stipend for summer research. The Harvard College Research Program ( HCRP ) is available during the academic year as well as the summer.  The Harvard University Center for the Environment ( HUCE ) has a summer undergraduate research program. The Harvard College Office of Undergraduate Research and Fellowships ( URAF ) has more information on these, as well as many other programs.

Students that were granted Federal Work Study as part of their financial aid package can use their Work Study award to conduct undergraduate research as well (research positions should note that they are work-study eligible to utilize this funding source).  

Research labs may have funding available to pay students directly, though we encourage you to seek out one of the many funding options available above first.

Yes! Some students choose to do research for course credit instead of for a stipend. To do so for a SEAS concentrations, students must enroll in one of the courses below and submit the relevant Project Application Form on the Course’s Canvas Page:

• Applied Mathematics 91r (Supervised Reading and Research)
• Computer Science 91r (Supervised Reading and Research)
• Engineering Sciences 91r (Supervised Reading and Research)

In general, you should expect to spend a minimum of one semester or one summer working on a project. There are many benefits to spending a longer period of time dedicated to a project. It’s important to have a conversation early with your research PI (“Principal Investigator”, the faculty who runs your research lab or program) to discuss the intended timeline of the first phase of your project, and there will be many additional opportunities to discuss how it could be extended beyond that.

For students who are satisfied with their research experience, remaining in one lab for the duration of their undergraduate careers can have significant benefits. Students who spend two or three years in the same lab often find that they have become fully integrated members of the research group. In addition, the continuity of spending several years in one lab group often allows students to develop a high level of technical expertise that permits them to work on more sophisticated projects and perhaps produce more significant results, which can also lead to a very successful senior thesis or capstone design project. 

However, there is not an obligation to commit to a single lab over your time at Harvard, and there are many reasons you may consider a change:

• your academic interests or concentration may have changed and thus the lab project is no longer appropriate
• you would like to study abroad (note that there is no additional cost in tuition for the term-time study abroad and Harvard has many fellowships for summer study abroad programs)
• your mentor may have moved on and there is no one in the lab to direct your project (it is not unusual for a postdoctoral fellow who is co-mentoring student to move as they secure a faculty position elsewhere)
• the project may not be working and the lab hasn’t offered an alternative
• or there may be personal reasons for leaving.  It is acceptable to move on

If you do encounter difficulties, but you strongly prefer to remain in the lab, get help.  Talk to your PI or research mentor, your faculty advisor or concentration advisor, or reach out to [email protected] for advice. The PI may not be aware of the problem and bringing it to their attention may be all that is necessary to resolve it.

Accepting an undergraduate into a research group and providing training for them is a very resource-intensive proposition for a lab, both in terms of the time commitment required from the lab mentors as well as the cost of laboratory supplies, reagents, computational time, etc. It is incumbent upon students to recognize and respect this investment.

• One way for you to acknowledge the lab’s investment is to show that you appreciate the time that your mentors set aside from their own experiments to teach you. For example, try to be meticulous about letting your mentor know well in advance when you are unable to come to the lab as scheduled, or if you are having a hard time making progress. 
• On the other hand, showing up in the lab at a time that is not on your regular schedule and expecting that your mentor will be available to work with you is unrealistic because they may be in the middle of an experiment that cannot be interrupted for several hours. 
• In addition to adhering to your lab schedule, show you respect the time that your mentor is devoting to you by putting forth a sincere effort when you are in the lab.  This includes turning off your phone, ignoring text messages, avoiding surfing the web and chatting with your friends in the lab etc. You will derive more benefit from a good relationship with your lab both in terms of your achievements in research and future interactions with the PI if you demonstrate a sincere commitment to them.
• There will be “crunch” times, maybe even whole weeks, when you will be unable to work in the lab as many hours as you normally would because of midterms, finals, paper deadlines, illness or school vacations. This is fine and not unusual for students, but remember to let your mentor know in advance when you anticipate absences. Disappearing from the lab for days without communicating with your mentor is not acceptable. Your lab mentor and PI are much more likely to be understanding about schedule changes if you keep the lines of communication open but they may be less charitable if you simply disappear for days or weeks at a time. From our conversations with students, we have learned that maintaining good communication and a strong relationship with the lab mentor and/or PI correlates well with an undergraduate’s satisfaction and success in the laboratory.
• Perhaps the best way for you to demonstrate your appreciation of the lab’s commitment is to approach your project with genuine interest and intellectual curiosity. Regardless of how limited your time in the lab may be, especially for first-years and sophomores, it is crucial to convey a sincere sense of engagement with your project and the lab’s research goals. You want to avoid giving the impression that you are there merely to fulfill a degree requirement or as a prerequisite for a post-graduate program.

There are lots of ways to open a conversation around how to get involved with research.

• For pre-concentrators: Talk to a student who has done research. The Peer Concentration Advisor (PCA) teams for Applied Math , Computer Science and Engineering mention research in their bios and would love to talk about their experience. Each PCA team has a link to Find My PCA which allows you to be matched with a PCA based on an interest area such as research. 
• For SEAS concentrators: Start a conversation with your ADUS, DUS, or faculty advisor about faculty that you are interested in working with. If you don’t have a list already, start with faculty whose courses you have taken or faculty in your concentration area. You may also find it helpful to talk with graduate student TFs in your courses about the work they are doing, as well as folks in the Active Learning Labs, as they have supported many students working on research and final thesis projects.
• For all students: Attend a SEAS Research Open House event to be connected with lab representatives that are either graduate students, postdocs, researchers or the PI for the labs. If you can’t attend the event, contact information is also listed on the Undergraduate Research Canvas page for follow-up in the month after the event is hosted. 

For any student who feels like they need more support to start the process, please reach out to [email protected] so someone from the SEAS Taskforce for Undergraduate Research can help you explore existing resources on the Undergraduate Research Canvas page . We especially encourage first-generation and students from underrepresented backgrounds to reach out if you have any questions.

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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.

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Main Project Topics for Computer Science

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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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Education Related Topics

Computer Engineering Research Topics

Computer Engineering Research Topics: Computer engineering is a dynamic field that encompasses a wide range of research areas, from hardware design to software development and everything in between. Here are some current and relevant research topics in computer engineering:

• Hardware Trojans Detection and Prevention: Develop methods to detect and mitigate malicious hardware modifications in integrated circuits.
• Hardware Security Primitives: Research novel cryptographic and security primitives implemented at the hardware level.
• Quantum Hardware Design: Investigate the development of quantum processors and qubit technologies.
• Quantum Algorithms: Explore quantum algorithms for solving complex computational problems.
• Exascale Computing: Study the design challenges and opportunities in building supercomputers capable of performing exascale computations.
• Parallel Computing: Research techniques for efficient parallel processing and multi-core architectures.
• Field-Programmable Gate Array (FPGA) Optimization: Develop methods for optimizing FPGA designs for performance and energy efficiency.
• Application-Specific Integrated Circuits (ASICs): Investigate ASIC design methodologies for specialized applications like AI and cryptography.
• Neuromorphic Hardware Design: Research neuromorphic hardware architectures that mimic the brain’s processing capabilities.
• Brain-Computer Interfaces: Explore the development of interfaces between neuromorphic hardware and biological systems.
• Reconfigurable Hardware Platforms: Investigate reconfigurable computing architectures for dynamic workload adaptation.
• Runtime Reconfiguration: Develop techniques for on-the-fly reconfiguration of hardware resources to optimize performance.
• Energy-Efficient IoT Devices: Research low-power hardware and communication protocols for IoT devices.
• Embedded System Security: Investigate security mechanisms for protecting embedded systems in IoT applications.
• Advanced Processor Design: Explore novel processor architectures, such as RISC-V, for improved performance and power efficiency.
• Memory Hierarchy Optimization: Research techniques for optimizing memory hierarchies to reduce latency and energy consumption.
• Green Computing: Investigate hardware and software techniques for reducing energy consumption in data centers and computing systems.
• Energy-Harvesting Systems: Research energy-harvesting solutions for powering low-power embedded devices.
• DNA Computing: Explore the use of DNA-based computing for solving complex computational problems.
• Biomimetic Hardware: Investigate hardware architectures inspired by biological systems.
• Co-Design for AI Accelerators: Develop hardware and software co-design methodologies for AI and machine learning accelerators.
• Heterogeneous Computing: Study the integration of different processing units, such as CPUs, GPUs, and FPGAs, for optimized performance.
• Robotics Hardware: Research hardware platforms for autonomous robots, drones, and industrial automation.
• Humanoid Robot Design: Investigate the design of humanoid robots for various applications, including healthcare and manufacturing.
• Very Large-Scale Integration (VLSI) Testing: Develop efficient testing methodologies for complex integrated circuits.
• VLSI CAD Tools: Create advanced tools for automating the design and verification of VLSI circuits.

These research topics reflect the diverse and evolving nature of computer engineering. Researchers in this field are continually working to advance hardware and software technologies, address emerging challenges, and drive innovation in various applications, including computing, communication, healthcare, and beyond. When choosing a research topic in computer engineering, consider your interests, expertise, and the potential impact of your research on advancing technology and solving real-world problems.

Steve George is Blogger, a marketer and content writer. He has B.A. in Economics from the University of Washington. Read more about Mzuri Mag .

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35 Design Engineering Project Topics for Computer Engineering

Imagine being the architect of the digital world, where your ideas transform into real solutions. That’s the essence of design engineering in computer science. It’s a dynamic field that empowers computer engineering students to craft innovative answers to real-world challenges. These projects aren’t just assignments; they’re the launchpad for your career.

But, the first step to this exciting journey is crucial: choosing the perfect project topic. Your choice will shape your learning and future opportunities. In this blog, we’ll dive into the world of design engineering, offering insights into what it’s all about. We’ll also guide you through the process of selecting the ideal project topic. And, to spark your imagination, we’ve curated a list of design engineering project topics for computer engineering students. Let’s embark on this creative adventure together!

What is Design Engineering?

Table of Contents

Design engineering is a multidisciplinary field that combines principles from various engineering disciplines to design and develop systems, products, or solutions. In the context of computer engineering, design engineering focuses on creating hardware and software solutions that address specific challenges or needs. These projects typically involve problem identification, conceptualization, design, prototyping, testing, and implementation.

Design engineering projects in computer engineering can range from developing new software applications, designing innovative hardware components, optimizing existing systems, or even tackling broader challenges like sustainable technology or healthcare solutions.

How Do I Choose My Design Engineering Project Topics?

Choosing the right design engineering project topics for computer engineering students is a crucial step in your design engineering journey. Here are some steps to help you make an informed decision:

1. Identify Your Interests

Start by considering your interests and passions within the field of computer engineering. Do you enjoy programming, hardware design, networking, or robotics? Your project should align with your interests to keep you motivated throughout the process.

2. Assess Your Skills

Take stock of your technical skills and knowledge. Are you more proficient in software development, electronics, or both? Your project should challenge you but also be achievable based on your current skill set.

3. Seek Guidance

Consult with professors, mentors, or professionals in the field. They can provide valuable insights, suggest project ideas, and help you refine your project proposal.

4. Define a Problem

Identify a real-world problem or challenge that your project can address. Your project should have a clear purpose and potential impact.

5. Consider Resources

Think about the resources you’ll need for your project. Do you have access to the necessary equipment, software, or support? Ensure your project is feasible within your constraints.

6. Research Existing Projects

Review existing design engineering projects to see what has been done before. This can help you identify gaps or areas where you can innovate.

Innovative Design Engineering Project Topics for Computer Engineering

Here is a diverse list of design engineering project topics for computer engineering students. These topics cover a wide range of subfields and challenges within computer engineering:

Hardware Projects

Let’s dive into the design engineering project topics for computer engineering students in hardware:

1. Smart Home Automation System: Design a system that allows users to control and monitor their home appliances remotely.

2. Gesture Recognition Device: Create a device that can recognize and interpret hand gestures for various applications.

3. Wearable Health Monitor: Develop a wearable device that tracks vital signs and sends data to a smartphone app.

4. IoT-based Agriculture Solution: Design an IoT system to optimize farming practices, including soil moisture monitoring and automated irrigation.

5. Low-Cost 3D Printer: Build an affordable 3D printer for educational or prototyping purposes.

6. Robotics Arm: Create a robotic arm with various applications, such as manufacturing or healthcare.

Software Projects

Discover some design engineering project topics for computer engineering students in software:

1. Machine Learning for Predictive Maintenance: Develop a predictive maintenance system using machine learning to reduce equipment downtime.

2. Natural Language Processing Chatbot: Build a chatbot that can understand and respond to natural language queries.

3. Virtual Reality (VR) Game: Create an immersive VR game or simulation for entertainment or educational purposes.

4. Mobile App for Disaster Management: Design a mobile app that provides real-time information and assistance during disasters.

5. Blockchain-Based Secure Voting System: Develop a secure and transparent electronic voting system using blockchain technology.

6. Traffic Management System: Build a smart traffic management system that optimizes traffic flow and reduces congestion.

Networking Projects

Now, let’s explore some easy design engineering project topics for computer engineering students in networking:

1. 5G Network Optimization: Optimize 5G network performance and reliability in a specific area.

2. IoT-Based Energy Monitoring: Create an IoT solution to monitor and control energy consumption in homes or businesses.

3. Network Security Analyzer: Develop a tool that can detect and mitigate network security threats in real time.

4. Software-defined networking (SDN) Controller: Build an SDN controller for efficient network management and customization.

5. VoIP (Voice over Internet Protocol) System: Design a VoIP system for voice communication over the Internet. incorporating the seamless integration of USA virtual phone numbers . These virtual phone numbers not only enhance accessibility but also provide a flexible solution for users to make and receive calls from different geographical locations, contributing to efficient communication infrastructure.

6. Cloud-Based Network Traffic Analyzer: Develop a cloud-based solution that captures and analyzes network traffic data for performance optimization.

Embedded Systems Projects

Here are some design engineering project topics for computer engineering students in embedded systems:

1. Smart Traffic Lights: Create an intelligent traffic light system that adapts to traffic flow.

2. Automated Plant Watering System: Build a system that automatically waters plants based on soil moisture levels.

3. Home Energy Management System: Design a system that optimizes energy usage in homes using IoT sensors.

4. Smart Mirror: Develop a mirror that displays useful information, such as weather updates and calendar events.

5. IoT-Based Health Monitoring Wearable: Create a wearable device for continuous health monitoring and data collection.

6. Autonomous Robotic Vacuum Cleaner: Design and develop an autonomous robotic vacuum cleaner that can navigate and clean rooms efficiently while avoiding obstacles.

Cybersecurity Projects

Discover some design engineering project topics for computer engineering students in cybersecurity:

1. Network Intrusion Detection System: Build an intrusion detection system to identify and mitigate network attacks.

2. Ransomware Protection Tool: Develop a tool to protect against ransomware attacks and data encryption.

3. Password Manager App: Create a secure password manager app for users to store and manage their passwords.

4. Biometric Authentication System: Design a biometric-based authentication system for enhanced security.

5. Secure File Encryption: Develop a secure file encryption and decryption tool.

6. IoT Security Framework: Develop a comprehensive security framework for Internet of Things (IoT) devices and networks.

Artificial Intelligence (AI) Projects

Let’s dive into the design engineering project topics for computer engineering students in AI.

1. AI-Based Personal Assistant: Create a virtual personal assistant powered by AI for tasks like scheduling and information retrieval.

2. Image Recognition System: Develop an image recognition system that can identify objects in images or videos.

3. AI-Based Music Generator: Build an AI-driven music composition tool.

4. Healthcare Diagnosis AI: Design an AI system for diagnosing medical conditions from patient data.

5. Autonomous Drone: Create an autonomous drone for tasks like surveillance or delivery.

6. AI-Powered Language Translation: Design an AI-driven language translation system capable of translating text or speech between multiple languages with high accuracy and efficiency. 

What Determines the Success of an Engineering Design Project?

The success of an engineering design project hinges on several key factors:

Clear Objectives

Success begins with well-defined project objectives. Understanding what needs to be achieved and setting clear goals is the foundation for the entire project.

Effective Planning

Comprehensive project planning, including resource allocation, timeline management, and risk assessment, is essential. A well-structured plan serves as a roadmap for the project’s execution.

Team Collaboration

Effective communication and collaboration among team members are critical. A cohesive team that works harmoniously can address challenges more efficiently.

Innovation and Creativity

Successful projects often involve innovative solutions that address the problem at hand more efficiently or elegantly. Creativity in design can set a project apart.

Adaptability

Flexibility in response to unexpected issues or changes in project requirements is vital. The ability to adapt and make informed decisions in real time is a hallmark of successful engineering projects.

Quality Control

Ensuring that the final product or solution meets high-quality standards is non-negotiable. Rigorous testing and quality control processes are essential.

Client Satisfaction

Ultimately, the success of a project is often determined by whether it meets or exceeds client expectations. Regular feedback and client engagement throughout the project are crucial.

Cost and Resource Management

Staying within budget and efficiently managing resources are essential for project success. Cost overruns can lead to project delays and dissatisfaction.

Timely Delivery

Meeting project deadlines is a key success factor. Delays can result in increased costs and a negative impact on stakeholders.

Documentation and Knowledge Transfer

Proper documentation of the project’s design, implementation, and maintenance processes ensures that the project’s success is sustainable over time. Knowledge transfer to relevant parties is crucial for long-term success.

Choosing the right design engineering project topics for computer engineering students is crucial for your academic and professional growth. It should align with your interests and skills while addressing a real-world problem or challenge. The list of project ideas provided here covers a wide spectrum of computer engineering subfields, from hardware and software development to networking, cybersecurity, embedded systems, and artificial intelligence . Remember to consult with mentors, conduct thorough research, and consider available resources before finalizing your project topic. With the right project, you can gain valuable experience and contribute to the advancement of computer engineering technology.

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150+ Best Engineering Research Topics for Students To Consider

Table of Contents

Engineering is a wide field of study that is divided into various branches such as Civil, Electrical, Mechanical, Electronics, Chemical, etc. Basically, each branch has thousands of engineering research topics to focus on. Hence, when you are asked to prepare an engineering research paper or dissertation for your final year assignments, you might experience difficulties with identifying a perfect topic. But hereafter, you need not worry about topic selection because to make the topic selection process easier for you, here we have suggested some tips for choosing a good engineering research topic. Additionally, we have also shared a list of the best 150+ engineering research paper topics on various specializations. Continue reading this blog to get exclusive ideas for engineering research paper writing.

Engineering Research Paper Topic Selection Tips

When it comes to research in the field of engineering, identifying the best engineering research topic is the first step. So, during that process, in order to identify the right topic, consider the following tips.

• Choose a topic from the research area matching your interest.
• Give preference to a topic that has a large scope to conduct research activities.
• Pick a topic that has several reference materials and evidence supporting your analysis.
• Avoid choosing an already or frequently discussed topic. If the topic is popular, discuss it from a different perspective.
• Never choose a larger topic that is tough to complete before the deadline.
• Finalize the topic only if it satisfies your academic requirements.

List of the Best Engineering Research Topics

Are you searching for the top engineering project ideas? Would you have to complete your academic paper on the best engineering research topic? If yes, then take a look below. Here, we have suggested a few interesting engineering topics in various disciplines that you can consider for your research or dissertation.

Mechanical Engineering Research Topics

• How does the study of robotics benefit from a mechanical engineering background?
• How can a new composite substitute reduce costs in large heat exchangers?
• Which will become the predominant energy technology this century?
• Why structural analysis is considered the foundation of mechanical engineering?
• Why is cast iron used in the engines of large ships?
• What is the finite element approach and why is it essential?
• Why is the flow of fluids important in mechanical engineering?
• What impact does mechanical engineering have in the medical field?
• How do sports incorporate mechanical engineering theories?
• What is the process of thermal heat transfer in machines?
• How can solar panels reduce energy costs in developing countries?
• In what ways is mechanical engineering at the forefront of the field?
• How do various elements interact differently with energy?
• How can companies improve manufacturing through new mechanical theories?

Additional Research Paper Topics on Mechanical Engineering

• Power generation: Extremely low emission technology.
•   Rail and wheel wear during the presence of third-body materials.
•  Studying the impact of athletic shoe properties on running performance and injuries
• Evaluating teeth decay using patient-specific tools
•   Nanotechnology.
• Describe the newly developed methods and applications in Vibration Systems
• Perspective or general Commentaries on the methods and protocols relevant to the research relating to Vibration Systems
• Software-related technology for Visibility of end-to-end operations for employee and management efficiencies
• What should be the best strategies to apply in the planning for consumer demand and responsiveness using data analytics
• Analysis of the monitoring of manufacturing processes using IOT/AI
• Critical analysis of the advancing digital manufacturing with artificial intelligence (AI) and machine learning (ML) Data Analytics
• Pyrolysis and Oxidation for Production and Consumption of Strongly Oxygenated Hydrocarbons as Chemical Energy Carriers: Explain
• Explore the most effective strategies for fatigue-fracture and failure prevention of automotive engines and the importance of such prevention
• Explore the turbomachinery performance and stability enhancement by means of end-wall flow modification
• Production optimization, engine performance, and tribological characteristics of biofuels and their blends in internal combustion engines as alternative fuels: Explain

Civil Engineering Research Topics

• The use of sustainable materials for construction: design and delivery methods.
• State-of-the-art practice for recycling in the construction industry.
• In-depth research on the wastewater treatment process
• Building Information Modelling in the construction industry
• Research to study the impact of sustainability concepts on organizational growth and development.
• The use of warm-mix asphalt in road construction
• Development of sustainable homes making use of renewable energy sources.
• The role of environmental assessment tools in sustainable construction
• Research to study the properties of concrete to achieve sustainability.
• A high-level review of the barriers and drivers for sustainable buildings in developing countries
• Sustainable technologies for the building construction industry
• Research regarding micromechanics of granular materials.
• Research to set up remote sensing applications to assist in the development of sustainable construction techniques.
• Key factors and risk factors associated with the construction of high-rise buildings.
• Use of a single-phase bridge rectifier
• Hydraulic Engineering: A Brief Overview
• Application of GIS techniques for planetary and space exploration
•   Reengineering the manufacturing systems for the future.
• Production Planning and Control.
•   Project Management.
•   Quality Control and Management.
•   Reliability and Maintenance Engineering.

Environmental Engineering Research Paper Topics

• Design and development of a system for measuring the carbon index of energy-intensive companies.
• Improving processes to reduce kWh usage.
• How can water conductivity probes help determine water quality and how can water be reused?
• A study of compressor operations on a forging site and mapping operations to identify and remove energy waste.
• A project to set up ways to measure natural gas flow ultrasonically and identify waste areas.
• Developing a compact device to measure energy use for a household.
• What are carbon credits and how can organizations generate them?
• Production of biogas is from organic coral waste.
• Analyzing the impact of the aviation industry on the environment and the potential ways to reduce it.
• How can voltage reduction devices help organizations achieve efficiency in electricity usage?
• What technologies exist to minimize the waste caused by offshore drilling?
• Identify the ways by which efficient control systems using information systems can be introduced to study the energy usage in a machining factory.
• The process mapping techniques to identify bottlenecks for the supply chain industry.
• Process improvement techniques to identify and remove waste in the automotive industry.
• In what ways do green buildings improve the quality of life?
• Discussion on the need to develop green cities to ensure environmental sustainability
• Process of carbon dioxide sequestration, separation, and utilization
• Development of facilities for wastewater treatment

Read more topics: Outstanding Environmental Science Topics for You to Consider

Electrical Engineering Research Topics

• Research to study transformer losses and reduce energy loss.
• How does an ultra-low-power integrated circuit work?
• Setting up a control system to monitor the process usage of compressors.
• Integration of smart metering pulsed outputs with wireless area networks and access to real-time data.
• What are the problems of using semiconductor topology?
• Developing effective strategies and methodical systems for paying as-you-go charging for electric vehicles.
• A detailed review and investigation into the key issues and challenges facing rechargeable lithium batteries.
• Trends and challenges in electric vehicles technologies
• Research to investigate, develop and introduce schemes to ensure efficient energy consumption by electrical machines.
• What is meant by regenerative braking?
• Smart charging of electric vehicles on the motorway
• Research to study metering techniques to control and improve efficiency.
• Develop a scheme to normalize compressor output to kWh.
• Research to introduce smart metering concepts to ensure efficient use of electricity.
• What is the most accurate method of forecasting electric loads?
• Fundamentals of Nanoelectronics
• Use of DC-to-DC converter in DC (Direct Current) power grid
• Development of Microgrid Integration

Electronics and Communications Engineering Research Topics

• Developing the embedded communication system for the national grid to optimize energy usage.
• Improvement of inter-symbol interference in optical communications.
• Defining the boundaries of electrical signals for current electronics systems.
• The limitation of fiber optic communication systems and the possibility of improving their efficiency.
• Gaussian pulse analysis and the improvement of this pulse to reduce errors.
• A study of the various forms of errors and the development of an equalization technique to reduce the error rates in data.
• Realizing the potential of RFID in the improvement of the supply chain.
• Design of high-speed communication circuits that effectively cut down signal noise.
• Radiation in integrated circuits and electronic devices.
• Spectral sensing research for water monitoring applications and frontier science and technology for chemical, biological, and radiological defense.

Computer and Software Engineering Research Topics

• How do businesses benefit from the use of data mining technologies?
• What are the risks of implementing radio-controlled home locks?
• To what extent should humans interact with computer technologies?
• Are financial trading systems operating over the web putting clients at risk?
• What challenges do organizations face with supply chain traceability?
• Do chatbot technologies negatively impact customer service?
• What does the future of computer engineering look like?
• What are the major concepts of software engineering?
• Are fingerprint-based money machines safe to use?
• What are the biggest challenges of using different programming languages?
• The role of risk management in information technology systems of organizations.
• In what ways does MOOD enhancement help software reliability?
• Are fingerprint-based voting systems the way of the future?
• How can one use an AES algorithm for the encryption of images?
• How can biological techniques be applied to software fault detection?

Read more: Creative Capstone Project Ideas For Students

Network and Cybersecurity Engineering Research Topics

• Write about Cybersecurity and malware connection.
• How to detect mobile phone hacking.
• Discuss Network intrusion detection and remedies.
• How to improve network security using attack graph models.
• Explain Modern virus encryption technology.
• Investigate the importance of algorithm encryption.
• Discuss the role of a firewall in securing networks.
• Write about the global cybersecurity strategy.
• Discuss the Privacy and security issues in chatbots.
• Write about Cloud security engineering specifics

Industrial Engineering Research Paper Topics

• The application of lean or Six Sigma in hospitals and services-related industries.
• The use of operation research techniques to reduce cost or improve efficiency.
• Advanced manufacturing techniques like additive manufacturing.
• Innovation as a Complex Adaptive System.
• CAD-based optimization in any manufacturing environment.
• Gap analysis in any manufacturing firm.
• The impact of 3D printing in the manufacturing sector.
• Simulating a real-life manufacturing environment into simulating software
• The rise of design and its use in the developing world.
• Building a network-based methodology to model supply chain systems.
• Risk optimization With P-order comic constraint
• Technology and its impact on mass customization
• How project management becomes more complex with disparate teams and outsourced functions?
• Scheduling problem for health care patients.

Biomedical Engineering Research Ideas

• How does the use of medical imaging help patients with higher risks?
• How can rehabilitation techniques be used to improve a patient’s quality of life?
• In what ways can biomaterials be used to deliver medications more efficiently?
• What impact does medical virtual reality have on a patient’s care?
• What advancements have been made in the field of neural technology?
• How does nanotechnology pave the way for further advancements in this field?
• What is computational biology and how does it impact our lives?
• How accurate are early diagnosis systems in detecting heart diseases?
• What does the future hold for technology-fueled medications?
• What are the guiding principles of biomedical engineering research?

Read more: Top Biology Research Topics for Academic Writing

Chemical Engineering Research Topics

• How can epoxy resins withstand the force generated by a firing gun?
• The use of software affected design aspects in chemical engineering.
• What challenges are there for biochemical engineering to support health?
• The advancements of plastic technology in the last half-century.
• How can chemical technologies be used to diagnose diseases?
• What are the most efficient pathways to the development of biofuels?
• How can charcoal particles be used to filter water in developing countries?
• Increased production of pharmacy drugs in many countries.
• How do complex fluids and polymers create more sustainable machinery?

Miscellaneous Engineering Research Ideas

• Sensing and controlling the intensity of light in LEDs.
• Design and development of a pressure sensor for a solar thermal panel.
• Development of microsensors to measure oil flow rate in tanks.
• How can organizations achieve success by reducing bottlenecks in the supply chain?
• Research to identify efficient logistics operations within a supply chain.
• Developing frameworks for sustainable assessments taking into account eco-engineering measures.
• Research to identify process improvement plans to support business strategies.
• What can engineers do to address the problems with climate change?
• The impact of training on knowledge performance index within the supply chain industry.
• Research to introduce efficiency within information systems and support the timely transfer of knowledge and information.

Final Words

Out of the 150+ engineering research paper topics and ideas suggested in this blog, choose any topic that is convenient for you to conduct research and write about. In case, you have not yet identified a good topic for your engineering research paper, reach out to us immediately. We have numerous PhD-certified experts in various engineering branches to offer help with research paper topic selection, writing, and editing in accordance with your requirements.

Especially, with the support of our scholarly writers, engineering students of all academic levels can complete their assignments on time and achieve the highest possible grades. Furthermore, taking our engineering assignment help would aid you in submitting high-quality and plagiarism-free research papers with proper citations and supporting evidence.

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BU Electrical Engineer Vivek Goyal Named a 2024 Guggenheim Fellow

The award recognizes goyal’s groundbreaking work in computational imaging, including research to photograph objects hidden by walls and around corners.

New Guggenheim Fellow Vivek Goyal, a College of Engineering professor of electrical and computer engineering.

Alene Bouranova

Cydney scott.

He might spend his days testing computer chips for the most minute of devices, or developing tech spies could use while on covert assignments. But if you ask Vivek Goyal , a Boston University College of Engineering professor and associate chair of doctoral programs for electrical and computer engineering, to name one of the coolest things about his job, he doesn’t pick inventing technology or testing gadgets.

“I really love the generation and analysis of probabilistic models,” Goyal admits.

These prediction-making algorithms might not be as glamorous as aiding secret agents, but they play a critical role in his burgeoning research on improving microscope imaging. That research is in part what earned Goyal a Guggenheim Fellowship , a prestigious grant from the John Simon Guggenheim Memorial Foundation. 

Each year, the foundation awards approximately 180 fellowship grants to individuals making significant contributions in the natural sciences, the social sciences, the creative arts, and the humanities. “Guggenheim Fellowships are intended for mid-career individuals who have demonstrated exceptional capacity for productive scholarship or exceptional creative ability in the arts and exhibit great promise for their future endeavors,” according to the foundation’s website.

“Vivek is the third College of Engineering faculty member to be awarded a Guggenheim Fellowship in recent years, which speaks to the outstanding depth and quality of research at the college,” says Elise Morgan , ENG dean ad interim. “Professor Goyal is a preeminent scholar and outstanding member of our faculty. His research on non-line-of-sight imaging—at the intersection of optics/photonics and computers and mathematics—has great potential for making the world safer for many people.” 

Goyal came to BU in 2014. Since then, his research has largely revolved around computational imaging—such as photon-efficient active imaging , where he’s shown how few photons are actually needed to capture crisp images with a camera, and non-line-of-sight imaging , where he uses surprisingly simple equipment to take photos of objects hidden from view. In one study , Goyal and his team used a pulsed laser and a single-photon detector array to take pictures of a post, mannequin, and staircase placed behind a wall, as well as to track moving objects. Goyal says the technology could eventually be used to aid rescue and armed forces teams, and to potentially improve vehicle warning systems.

“It is an incredible honor for Professor Goyal to join the exceptional group of artists, writers, scholars, and scientists awarded a Guggenheim Fellowship this year,” says Gloria Waters , BU’s vice president and associate provost for research. “This award, along with the multiple other distinguished awards he has received, is a clear recognition of the importance of his cutting-edge research.” Last year, Goyal was also named an American Association for the Advancement of Science (AAAS) Fellow .

This award, along with the multiple other distinguished awards he has received, is a clear recognition of the importance of his cutting-edge research. Gloria Waters

Recently, his research has also involved electron microscopes , high-resolution microscopes that form images of a specimen using a focused beam of particles, such as electrons or ions, instead of photons. His groundbreaking work in electron imaging has significant potential implications for biomedicine and manufacturing, among myriad other applications.

According to Goyal, his microscopes research is exciting, even a little off-the-wall, because it upends what have long been considered the fundamental limits of electron imaging.

“One thing I love about the Guggenheim Fellowship is that it’s an award based on both what you say you plan to do, but also on your track record of creativity,” Goyal says. “It’s very validating to feel like my track record was valued enough that this foundation wants to support me in trying to do something a little crazy.

“I take that compliment, and I appreciate it.”

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Alene Bouranova is a Pacific Northwest native and a BU alum (COM’16). After earning a BS in journalism, she spent four years at Boston magazine writing, copyediting, and managing production for all publications. These days, she covers campus happenings, current events, and more for BU Today . Fun fact: she’s still using her Terrier card from 2013. When she’s not writing about campus, she’s trying to lose her Terrier card so BU will give her a new one. She lives in Cambridge with her plants. Profile

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A Vision for the Future

April 11, 2024

College of Science and Engineering names new Department Chair and Amazon Endowed Chair of Computer Science whose research includes issues around cybersecurity.

Burkhard Englert, PhD, has been appointed the Department Chair and Amazon Endowed Chair of Computer Science. Dr. Englert currently serves as the Department Chair of Computer Science at Norfolk State University after being in the same position at the University of North Carolina-Wilmington. He will begin at Seattle University on July 1.

To this role Dr. Englert brings a wealth of experience and a long track record of leadership, advocacy and scholarly excellence. Those skills will be vital given the increasing popularity of computing programs, including software engineering, cybersecurity, information and data science and computer engineering, among others.

As the Department Chair and Amazon Chair, Dr. Englert will be able to support the growth of the College of Science and Engineering’s existing as well as new programs, both at the graduate and undergraduate levels. Additionally, he will help address persistent issues of equity and access in collaboration with the faculty and staff in the college. His vision for growth of computer science as a discipline as well as infusing computing around the campus is aligned with our strategic directions. 

“What I really like is Seattle University is always looking forward and the leadership deeply understands the challenges and opportunities that we face,” Dr. Englert says. “I’m excited to be part of that commitment. The faculty is outstanding. They see what the problems are and they aren’t running away—they’re looking to see what has to happen next. It won’t be easy, but it’s exciting to be a part of. I think it’s a perfect fit.”

Dr. Englert will be tasked with overseeing the implementation of a strategic vision for the department while supporting and hiring faculty. He also will work toward developing partnerships both within Seattle University and in industry to help attract students from diverse backgrounds. He says he is most excited to work with the outstanding faculty as they take on the challenge of an ever-changing cyber world, including the increasing use of and issues around artificial intelligence.

Beginning his career with teaching positions at UCLA and California State University Long Beach (CSULB), Dr. Englert did a two-year stint as coordinator of Graduate Programs in the College of Engineering at Cal State Long Beach. At Long Beach he also served as the Department Chair for Biomedical Engineering and Computer Engineering/Computer Science before moving on to Dean of the College of Natural and Health Sciences at the University of Northern Colorado, where he also served as Special Assistant of the Provost.

Dr. Englert has been honored for his work several times, including the CSULB Distinguished Faculty Teaching Award, the Robert Sorgenfrey Distinguished Teaching Award for the Department of Mathematics at UCLA, the Louis J. DeLuca Memorial Award as outstanding Teaching Assistant in the Department of Mathematics at the University of Connecticut as well as the CSULB Leadership Fellow and the Project NExT (New Experiences in Teaching) Fellow from the Southern California Section of the Mathematical Association of America.

Dr. Englert has a Bachelor of Science in Mathematics from University of Tübingen in Germany, a Master’s in Mathematics and a Master’s in Computer Science and Engineering from the University of Connecticut and a PhD in Mathematics (Theoretical Computer Science) from the University of Connecticut.

Much of Dr. Englert’s research involves issues of cybersecurity as well as machine learning, distributed computing, distributed algorithms and transportation network modeling and optimization. He has presented at conferences around the world.

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Millions of gamers advance biomedical research

Largest global citizen science project accelerates knowledge of human microbiome.

Leveraging gamers and video game technology can dramatically boost scientific research according to a new study published today in Nature Biotechnology .

4.5 million gamers around the world have advanced medical science by helping to reconstruct microbial evolutionary histories using a minigame included inside the critically and commercially successful video game, Borderlands 3 . Their playing has led to a significantly refined estimate of the relationships of microbes in the human gut. The results of this collaboration will both substantially advance our knowledge of the microbiome and improve on the AI programs that will be used to carry out this work in future.

Tracing the evolutionary relationships of bacteria

By playing Borderlands Science , a mini-game within the looter-shooter video game Borderlands 3 , these players have helped trace the evolutionary relationships of more than a million different kinds of bacteria that live in the human gut, some of which play a crucial role in our health. This information represents an exponential increase in what we have discovered about the microbiome up till now. By aligning rows of tiles which represent the genetic building blocks of different microbes, humans have been able to take on tasks that even the best existing computer algorithms have been unable to solve yet.

The project was led by McGill University researchers, developed in collaboration with Gearbox Entertainment Company, an award-winning interactive entertainment company, and Massively Multiplayer Online Science (MMOS), a Swiss IT company connecting scientists to video games), and supported by the expertise and genomic material from the Microsetta Initiative led by Rob Knight from the Departments of Pediatrics, Bioengineering, and Computer Science & Engineering at the University of California San Diego.

Humans improve on existing algorithms and lay groundwork for the future

Not only have the gamers improved on the results produced by the existing programs used to analyze DNA sequences, but they are also helping lay the groundwork for improved AI programs that can be used in future.

"We didn't know whether the players of a popular game like Borderlands 3 would be interested or whether the results would be good enough to improve on what was already known about microbial evolution. But we've been amazed by the results." says Jérôme Waldispühl, an associate professor in McGill's School of Computer Science and senior author on the paper published today. "In half a day, the Borderlands Science players collected five times more data about microbial DNA sequences than our earlier game, Phylo , had collected over a 10-year period."

The idea for integrating DNA analysis into a commercial video game with mass market appeal came from Attila Szantner, an adjunct professor in McGill's School of Computer Science and CEO and co-founder of MMOS. "As almost half of the world population is playing with videogames, it is of utmost importance that we find new creative ways to extract value from all this time and brainpower that we spend gaming," says Szantner. " Borderlands Science shows how far we can get by teaming up with the game industry and its communities to tackle the big challenges of our times."

"Gearbox's developers were eager to engage millions of Borderlands players globally with our creation of an appealing in-game experience to demonstrate how clever minds playing Borderlands are capable of producing tangible, useful, and valuable scientific data at a level not approachable with non-interactive technology and mediums," said Randy Pitchford, founder and CEO of Gearbox Entertainment Company. "I'm proud that Borderlands Science has become one of the largest and most accomplished citizen science projects of all time, forecasting the opportunity for similar projects in future video games and pushing the boundaries of the positive effect that video games can make on the world."

Relating microbes to disease and lifestyle

The tens of trillions of microbes that colonize our bodies play a crucial role in maintaining human health. But microbial communities can change over time in response to factors such as diet, medications, and lifestyle habits.

Because of the sheer number of microbes involved, scientists are still only in the early days of being able to identify which microorganisms are affected by, or can affect, which conditions. Which is why the researchers' project and the results from the gamers are so important.

"We expect to be able to use this information to relate specific kinds of microbes to what we eat, to how we age, and to the many diseases ranging from inflammatory bowel disease to Alzheimer's that we now know microbes to be involved in," adds Knight, who also directs the Center for Microbiome Innovation at the UC San Diego. "Because evolution is a great guide to function, having a better tree relating our microbes to one another gives us a more precise view of what they are doing within and around us."

Building communities to advance knowledge

"Here we have 4.5 million people who contributed to science. In a sense, this result is theirs too and they should feel proud about it," says Waldispühl. "It shows that we can fight the fear or misconceptions that members of the public may have about science and start building communities who work collectively to advance knowledge."

" Borderlands Science created an incredible opportunity to engage with citizen scientists on a novel and important problem, using data generated by a separate massive citizen science project," adds Daniel McDonald, the Scientific Director of the Microsetta Initiative. "These results demonstrate the remarkable value of open access data, and the scale of what is possible with inclusive practices in scientific endeavors."

• Microbes and More
• Evolutionary Biology
• Forensic Research
• Engineering
• Video Games
• Artificial Intelligence
• Computer Science
• Veterinary medicine
• Scientific visualization
• Computer and video games
• Full motion video
• Structural alignment (genomics)

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Journal Reference :

• Roman Sarrazin-Gendron, Parham Ghasemloo Gheidari, Alexander Butyaev, Timothy Keding, Eddie Cai, Jiayue Zheng, Renata Mutalova, Julien Mounthanyvong, Yuxue Zhu, Elena Nazarova, Chrisostomos Drogaris, Kornél Erhart, David Bélanger, Michael Bouffard, Joshua Davidson, Mathieu Falaise, Vincent Fiset, Steven Hebert, Dan Hewitt, Jonathan Huot, Seung Kim, Jonathan Moreau-Genest, David Najjab, Steve Prince, Ludger Saintélien, Amélie Brouillette, Gabriel Richard, Randy Pitchford, Sébastien Caisse, Mathieu Blanchette, Daniel McDonald, Rob Knight, Attila Szantner, Jérôme Waldispühl. Improving microbial phylogeny with citizen science within a mass-market video game . Nature Biotechnology , 2024; DOI: 10.1038/s41587-024-02175-6

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