how to write a research proposal computer science

Welcome to the on-line version of the UNC dissertation proposal collection. The purpose of this collection is to provide examples of proposals for those of you who are thinking of writing a proposal of your own. I hope that this on-line collection proves to be more difficult to misplace than the physical collection that periodically disappears. If you are preparing to write a proposal you should make a point of reading the excellent document The Path to the Ph.D., written by James Coggins. It includes advice about selecting a topic, preparing a proposal, taking your oral exam and finishing your dissertation. It also includes accounts by many people about the process that each of them went through to find a thesis topic. Adding to the Collection This collection of proposals becomes more useful with each new proposal that is added. If you have an accepted proposal, please help by including it in this collection. You may notice that the bulk of the proposals currently in this collection are in the area of computer graphics. This is an artifact of me knowing more computer graphics folks to pester for their proposals. Add your non-graphics proposal to the collection and help remedy this imbalance! There are only two requirements for a UNC proposal to be added to this collection. The first requirement is that your proposal must be completely approved by your committee. If we adhere to this, then each proposal in the collection serves as an example of a document that five faculty members have signed off on. The second requirement is that you supply, as best you can, exactly the document that your committee approved. While reading over my own proposal I winced at a few of the things that I had written. I resisted the temptation to change the document, however, because this collection should truely reflect what an accepted thesis proposal looks like. Note that there is no requirement that the author has finished his/her Ph.D. Several of the proposals in the collection were written by people who, as of this writing, are still working on their dissertation. This is fine! I encourage people to submit their proposals in any form they wish. Perhaps the most useful forms at the present are Postscript and HTML, but this may not always be so. Greg Coombe has generously provided LaTeX thesis style files , which, he says, conform to the 2004-2005 stlye requirements.

Many thanks to everyone who contributed to this collection!

Greg Coombe, "Incremental Construction of Surface Light Fields" in PDF . Karl Hillesland, "Image-Based Modelling Using Nonlinear Function Fitting on a Stream Architecture" in PDF . Martin Isenburg, "Compressing, Streaming, and Processing of Large Polygon Meshes" in PDF . Ajith Mascarenhas, "A Topological Framework for Visualizing Time-varying Volumetric Datasets" in PDF . Josh Steinhurst, "Practical Photon Mapping in Hardware" in PDF . Ronald Azuma, "Predictive Tracking for Head-Mounted Displays," in Postscript Mike Bajura, "Virtual Reality Meets Computer Vision," in Postscript David Ellsworth, "Polygon Rendering for Interactive Scientific Visualization on Multicomputers," in Postscript Richard Holloway, "A Systems-Engineering Study of the Registration Errors in a Virtual-Environment System for Cranio-Facial Surgery Planning," in Postscript Victoria Interrante, "Uses of Shading Techniques, Artistic Devices and Interaction to Improve the Visual Understanding of Multiple Interpenetrating Volume Data Sets," in Postscript Mark Mine, "Modeling From Within: A Proposal for the Investigation of Modeling Within the Immersive Environment" in Postscript Steve Molnar, "High-Speed Rendering using Scan-Line Image Composition," in Postscript Carl Mueller, " High-Performance Rendering via the Sort-First Architecture ," in Postscript Ulrich Neumann, "Direct Volume Rendering on Multicomputers," in Postscript Marc Olano, "Programmability in an Interactive Graphics Pipeline," in Postscript Krish Ponamgi, "Collision Detection for Interactive Environments and Simulations," in Postscript Russell Taylor, "Nanomanipulator Proposal," in Postscript Greg Turk, " Generating Textures on Arbitrary Surfaces ," in HTML and Postscript Terry Yoo, " Statistical Control of Nonlinear Diffusion ," in Postscript

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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Thesis Proposal

In the thesis proposal, the PhD or DES student lays out an intended course of research for the dissertation.  By accepting the thesis proposal, the student’s dissertation proposal committee agrees that the proposal is practicable and acceptable, that its plan and prospectus are satisfactory, and that the candidate is competent in the knowledge and techniques required, and formally recommends that the candidate proceed according to the prospectus and under the supervision of the dissertation committee. It is part of the training of the student’s research apprenticeship that the form of this proposal must be as concise as those proposals required by major funding agencies.

The student proposes to a committee consisting of the student’s advisor and two other researchers who meet requirements for dissertation committee membership.  The advisor should solicit the prospective committee members, not the student. In cases where the research and departmental advisors are different , both must serve on the committee.

The student prepares a proposal document that consists of a core, plus any optional appendices. The core is limited to 30 pages (e.g., 12 point font, single spacing, 1 inch margins all around), and should contain sections describing 1) the problem and its background, 2) the innovative claims of the proposed work and its relation to existing work, 3) a description of at least one initial result that is mature enough to be able to be written up for submission to a conference, and 4) a plan for completion of the research. The committee commits to read and respond to the core, but reserves the right to refuse a document whose core exceeds the page limit. The student cannot assume that the committee will read or respond to any additional appendices.

The complete doctoral thesis proposal document must be disseminated to the entire dissertation committee no later than two weeks (14 days) prior to the proposal presentation. The PhD Program Administrator must be informed of the scheduling of the proposal presentation no later than two weeks (14 days) prior to the presentation. Emergency exceptions to either of these deadlines can be granted by the Director of Graduate Studies or the Department Chair on appeal by the advisor and agreement of the committee.

A latex thesis proposal template is available here .

PRESENTATION AND FEEDBACK

The student presents the proposal in a prepared talk of 45 minutes to the committee, and responds to any questions and feedback by the committee.

The student’s advisor, upon approval of the full faculty, establishes the target semester by which the thesis proposal must be successfully completed. The target semester must be no later than the eighth semester, and the student must be informed of the target semester no later than the sixth semester.

The candidacy   exam  must be successfully completed  before  the  proposal can be attempted.  The proposal must be completed prior to submitting the application for defense. [Instituted by full faculty vote September 16, 2015.]

Passing or failing is determined by consensus of the committee, who then sign the dissertation proposal form (sent to advisors by phd-advising@cs.  Failure to pass the thesis proposal by the end of the target semester or the eighth semester, whichever comes first, is deemed unsatisfactory progress: the PhD or DES student is normally placed on probation and can be immediately dismissed from the program. However, on appeal of the student’s advisor, one semester’s grace can be granted by the full faculty.

Last updated on October 16, 2023.

Find open faculty positions here .

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This recent action provides a moment for us to collectively reflect on our community within Columbia Engineering and the importance of our commitment to maintaining an open and welcoming community for all students, faculty, researchers and administrative staff. As a School of Engineering and Applied Science, we are fortunate to attract students and faculty from diverse backgrounds, from across the country, and from around the world. It is a great benefit to be able to gather engineers and scientists of so many different perspectives and talents – all with a commitment to learning, a focus on pushing the frontiers of knowledge and discovery, and with a passion for translating our work to impact humanity.

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• Research Process

Writing a Scientific Research Project Proposal

• 5 minute read

Table of Contents

The importance of a well-written research proposal cannot be underestimated. Your research really is only as good as your proposal. A poorly written, or poorly conceived research proposal will doom even an otherwise worthy project. On the other hand, a well-written, high-quality proposal will increase your chances for success.

In this article, we’ll outline the basics of writing an effective scientific research proposal, including the differences between research proposals, grants and cover letters. We’ll also touch on common mistakes made when submitting research proposals, as well as a simple example or template that you can follow.

What is a scientific research proposal?

The main purpose of a scientific research proposal is to convince your audience that your project is worthwhile, and that you have the expertise and wherewithal to complete it. The elements of an effective research proposal mirror those of the research process itself, which we’ll outline below. Essentially, the research proposal should include enough information for the reader to determine if your proposed study is worth pursuing.

It is not an uncommon misunderstanding to think that a research proposal and a cover letter are the same things. However, they are different. The main difference between a research proposal vs cover letter content is distinct. Whereas the research proposal summarizes the proposal for future research, the cover letter connects you to the research, and how you are the right person to complete the proposed research.

There is also sometimes confusion around a research proposal vs grant application. Whereas a research proposal is a statement of intent, related to answering a research question, a grant application is a specific request for funding to complete the research proposed. Of course, there are elements of overlap between the two documents; it’s the purpose of the document that defines one or the other.

Scientific Research Proposal Format

Although there is no one way to write a scientific research proposal, there are specific guidelines. A lot depends on which journal you’re submitting your research proposal to, so you may need to follow their scientific research proposal template.

In general, however, there are fairly universal sections to every scientific research proposal. These include:

• Title: Make sure the title of your proposal is descriptive and concise. Make it catch and informative at the same time, avoiding dry phrases like, “An investigation…” Your title should pique the interest of the reader.
• Abstract: This is a brief (300-500 words) summary that includes the research question, your rationale for the study, and any applicable hypothesis. You should also include a brief description of your methodology, including procedures, samples, instruments, etc.
• Introduction: The opening paragraph of your research proposal is, perhaps, the most important. Here you want to introduce the research problem in a creative way, and demonstrate your understanding of the need for the research. You want the reader to think that your proposed research is current, important and relevant.
• Background: Include a brief history of the topic and link it to a contemporary context to show its relevance for today. Identify key researchers and institutions also looking at the problem
• Literature Review: This is the section that may take the longest amount of time to assemble. Here you want to synthesize prior research, and place your proposed research into the larger picture of what’s been studied in the past. You want to show your reader that your work is original, and adds to the current knowledge.
• Research Design and Methodology: This section should be very clearly and logically written and organized. You are letting your reader know that you know what you are going to do, and how. The reader should feel confident that you have the skills and knowledge needed to get the project done.
• Preliminary Implications: Here you’ll be outlining how you anticipate your research will extend current knowledge in your field. You might also want to discuss how your findings will impact future research needs.
• Conclusion: This section reinforces the significance and importance of your proposed research, and summarizes the entire proposal.
• References/Citations: Of course, you need to include a full and accurate list of any and all sources you used to write your research proposal.

Common Mistakes in Writing a Scientific Research Project Proposal

Remember, the best research proposal can be rejected if it’s not well written or is ill-conceived. The most common mistakes made include:

• Not providing the proper context for your research question or the problem
• Failing to reference landmark/key studies
• Losing focus of the research question or problem
• Not accurately presenting contributions by other researchers and institutions
• Incompletely developing a persuasive argument for the research that is being proposed
• Misplaced attention on minor points and/or not enough detail on major issues
• Sloppy, low-quality writing without effective logic and flow
• Incorrect or lapses in references and citations, and/or references not in proper format
• The proposal is too long – or too short

Scientific Research Proposal Example

There are countless examples that you can find for successful research proposals. In addition, you can also find examples of unsuccessful research proposals. Search for successful research proposals in your field, and even for your target journal, to get a good idea on what specifically your audience may be looking for.

While there’s no one example that will show you everything you need to know, looking at a few will give you a good idea of what you need to include in your own research proposal. Talk, also, to colleagues in your field, especially if you are a student or a new researcher. We can often learn from the mistakes of others. The more prepared and knowledgeable you are prior to writing your research proposal, the more likely you are to succeed.

Language Editing Services

One of the top reasons scientific research proposals are rejected is due to poor logic and flow. Check out our Language Editing Services to ensure a great proposal , that’s clear and concise, and properly referenced. Check our video for more information, and get started today.

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Yale undergraduate research, how to write a proposal.

The abstract should summarize your proposal. Include one sentence to introduce the problem you are investigating, why this problem is significant, the hypothesis to be tested, a brief summary of experiments that you wish to conduct and a single concluding sentence. (250 word limit)

Introduction

The introduction discusses the background and significance of the problem you are investigating. Lead the reader from the general to the specific. For example, if you want to write about the role that Brca1 mutations play in breast cancer pathogenesis, talk first about the significance of breast cancer as a disease in the US/world population, then about familial breast cancer as a small subset of breast cancers in general, then about discovery of Brca1 mutations in familial breast cancer, then Brca1’s normal functions in DNA repair, then about how Brca1 mutations result in damaged DNA and onset of familial breast cancer, etc. Definitely include figures with properly labeled text boxes (designated as Figure 1, Figure 2, etc) here to better illustrate your points and help your reader wade through unfamiliar science. (3 pages max)

Formulate a hypothesis that will be tested in your grant proposal. Remember, you are doing hypothesis-driven research so there should be a hypothesis to be tested! The hypothesis should be focused, concise and flow logically from the introduction. For example, your hypothesis could be “I hypothesize that overexpressing wild type Brca1 in Brca1 null tumor cells will prevent metastatic spread in a mouse xenograph model.” Based on your hypothesis, your Specific Aims section should be geared to support it. The hypothesis is stated in one sentence in the proposal. 

Specific Aims (listed as Specific Aim 1, Specific Aim 2)

This is where you will want to work with your mentor to craft the experimental portion of your proposal. Propose two original specific aims to test your hypothesis. Don’t propose more than two aims-you will NOT have enough time to do more. In the example presented, Specific Aim 1 might be “To determine the oncogenic potential of Brca1 null cell lines expressing wild type Brca1 cDNA”. Specific aim 2 might be “To determine the metastatic potential of Brca1 null cells that express WT Brca1”. You do not have to go into extensive technical details, just enough for the reader to understand what you propose to do. The best aims yield mechanistic insights-that is, experiments proposed address some mechanisms of biology. A less desirable aim proposes correlative experiments that does not address mechanistically how BRCA1 mutations generate cancer. It is also very important that the two aims are related but NOT interdependent. What this means is that if Aim 1 doesn’t work, Aim 2 is not automatically dead. For example, say you propose in Aim 1 to generate a BRCA1 knockout mouse model, and in Aim 2 you will take tissues from this mouse to do experiments. If knocking out BRCA1 results in early embryonic death, you will never get a mouse that yields tissues for Aim 2. You can include some of your mentor’s data here as “Preliminary data”. Remember to carefully cite all your sources. (4 pages max; 2 pages per Aim)

Potential pitfalls and alternative strategies

This is a very important part of any proposal. This is where you want to discuss the experiments you propose in Aims 1 and 2. Remember, no experiment is perfect. Are there any reasons why experiments you proposed might not work? Why? What will you do to resolve this? What are other possible strategies you might use if your experiments don’t work? If a reviewer spots these deficiencies and you don’t propose methods to correct them, your proposal will not get funded. You will want to work with your mentor to write this section. (1/2 page per Aim)

Cite all references, including unpublished data from your mentor. Last, First, (year), Title, Journal, volume, pages.

*8 page proposal limit (not including References), 1.5 spacing, 12pt Times New Roman font

• View an example of a research proposal submitted for the Yale College First-Year Summer Research Fellowship (PDF).  
• View an example of a research proposal submitted for the Yale College Dean’s Research Fellowship and the Rosenfeld Science Scholars Program (PDF) .

Tips on Writing a Computer Science Research Proposal

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Writing a research proposal in any discipline is a real challenge, but writing a computer science research proposal requires not only theoretical knowledge and the ability to dig in literature, but also practical experience and relevant background. It is only normal if you feel stuck just beginning to compose your research proposal. Along with the guidelines received from your professor and rules from textbooks, you can benefit from these expert tips written by expert academic writers at WriteMyPaperHub.com academic writing company . Use them to get started and keep going.

Clarity the Problem You Address

The basic idea is to find a field in Computer Science which was previously underestimated or not researched enough. Seeing this field, you need to identify a problem that evolves from this field being not studied enough or an issue that will be solved if you do proper research in this field. It is better if you find at least several problems in one area and present them to your professor or supervisor. He or she will choose one with you, and you can start working on it. Though in the research proposal the problem statement is not the first part of the paper, without deciding on it, you should not start writing at all, or it can all be gone in vain. Sometimes, it is not obligatory to confirm your problem statement with your professor or supervisor, but we strongly recommend to do it to share vision as well as responsibility for the chosen research direction. Someone would say that it sounds like manipulation, but we are sure it is just about efficiency.

Write a Detailed Research Plan

Writing a plan is boring, and it takes too much time, so students often tend to skip this step and go directly to writing. Unfortunately, this approach just doesn’t work. You need to write a thorough plan and confirm it with your professor or supervisor. Without a plan, you will get stuck too often, not knowing what to do next. Being stressed about it, you will start procrastinating, feeling that you don’t have enough progress you will get even more stressed. Lots of computer science research proposals were not submitted on time (or at all!) due to this circle of stress and procrastination. To avoid it, you need a detailed plan. In this case, even if something goes wrong, you always know where to pick up. There are two plans — the one you present to your professor, and the one you use for yourself. Think about yourself as about a manager of this computer science research project and write a plan considering risks, limitations, and strengths.

Elaborate On Research Methods

Unlike in many other disciplines, in Computer Science, methods often exist in the form of algorithms, and you need to prove why the chosen algorithms are the best for the particular research and problem in question. Most probably, you will use combined methods — one classical, and one or two specific for Computer Science. Sometimes, this part should also include time for completion and even budget, if you submit your research proposal as an admission document for grant or scholarship. Don’t overestimate your abilities to write fast and don’t underestimate the costs.

Just Know It’s Going to Be Better

Maybe this point sounds silly, but it is essential to know it. You need to remember, that writing a research proposal for the first time is always challenging, for everyone, not only for you. If you get stuck, it doesn’t mean you are not good enough, or you’ve chosen a wrong path, or you should do something else. More of it, writing a research proposal is often harder than doing research and writing a research report itself. When dealing with a proposal, you have to plan everything in advance, decide on a problem, write a problem (thesis) statement, choose literature, confirm every step, decide on methods, etc. Later you will use this preparation work for your more important papers, such as thesis or dissertation. Think in advance about hardships and come up with some small motivating treats for yourself.

When you are done with writing the main parts, start making formatting and checking your text with grammar and spelling checkers and anti-plagiarism software. Students often overestimate the amount of time they need to write research proposals and don’t have enough time in the end to make final check-ups. Make it a rule to check every 2-3 written pages gradually, and you will both improve your writing skills, rest a little, and make sure when the deadline becomes scarily close, almost entire paper is perfectly formatted and checked for mistakes and plagiarism. Following your plan and thinking in advance will lead you through this challenging period of writing a computer science research proposal. Good luck!

About Author

Bob Buskirk

About 10 years of computer experience. Been messing around with electronics since I was 5, got into computers when I was in highschool, been modding them ever since then. Very interested in how things work and their design.

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500+ Computer Science Research Topics

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

Computer Science Research Topics

Computer Science Research Topics are as follows:

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

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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Early in Michaelmas Term you need to submit a project proposal that describes what you plan to do and how you plan to evaluate it. In order to help with this process, you are assigned two Project Checkers, who, together with your Supervisor and Director of Studies, will provide advice on your ideas. The deadline for project proposals is a little over one week into term, and is a hard deadline .

Choosing a project

You have a great deal of freedom in the selection of a project, and should start narrowing down the possibilities by identifying starting points or ideas that appeal to you. These initial ideas should be refined to a coherent project plan, which is then submitted as the project proposal. The proposal will be discussed informally with your Project Checkers, but is then submitted to the Head of the Department as a formal statement of intent.

The main sources of inspiration are commonly:

• Ideas proposed by candidates.
• Suggestions made by Supervisors or Directors of Studies.
• The project suggestions on the projects web page .
• Past years’ projects. Most recent dissertations are available to read online ,
• Proposals put forward by industry, especially companies who have provided vacation employment for students.

When ideas are first suggested or discussed it is good to keep an open mind about them—a topic that initially seems very interesting may prove unreasonable on further consideration, perhaps because it will be too difficult. Equally, many ideas on topics that are unfamiliar to you will need study before you can appreciate what would be involved in following them. Almost all project suggestions should also be seen as starting points rather than fully worked out proposals.

Notes on project choice

Some project ideas can be discarded very quickly as inappropriate. It is almost always best to abandon a doubtful idea early on rather than to struggle to find a slant that will allow the Project Checkers to accept it. Projects are expected to have a significant Computer Science content; for example, writing an application program or game-playing program, where the main intellectual effort relates to the area supported rather than to the computation, are not suitable. Projects must also be about the right size to fit into the time available. The implications of this will best be judged by looking at past years’ projects and by discussing plans with a Supervisor or Project Checker. They should not allow you to waste much time considering either ideas that would prove too slight or ones that are grossly overambitious.

It is important to pick a project that has an achievable core and room for extension. You should pick a suitably challenging project, where you will likely have to learn new things in order to successfully complete it. In addition, it is expected that you will make use of existing libraries and tools (i.e. don’t reinvent the wheel) unless there is a good reason for producing your own implementation.

Re-use of projects that have been attempted in the past

Projects are intended to give you a chance to display your abilities as a computer scientist. You are not required (or indeed expected) to conduct research or produce radically new results. It is thus perfectly proper to carry out a project that has been attempted before, and it is commonplace to have two students in the same year both basing their projects on the same original idea.

In such cases it is not acceptable to run a simple action replay of a previous piece of work. Fortunately all projects of the required scale provide considerable scope for different approaches; producing a new variation on an existing theme will not be hard. Furthermore the report produced at the end of a previous attempt at a project will often identify areas that led to unexpected difficulties, or opportunities for new developments—both these provide good scope for putting a fresh slant on the ideas involved.

Supervision

In some cases the most critical problem will be finding a suitable project Supervisor, somebody whom you will see regularly to report your progress and obtain guidance about project work throughout the year. This might be one of your main course Supervisors or a separate, specialist project Supervisor, but it should not be assumed that a person suggesting a project will be willing to supervise it. Supervisors have to be appointed by your Director of Studies, but in most cases it will be left up to you to identify somebody willing and able to take on the task. The Project Checkers will be interested only in seeing that someone competent has agreed to supervise the project, and that your Director of Studies is content with that arrangement.

Each project will have a number of critical resources associated with its completion. If even one of these fails to materialise then it will not be possible to proceed with a project based on the idea; your Director of Studies can help you judge what might be a limiting issue.

The project proposal must contain as its last section a Resources Declaration. This must explicitly list the resources needed and give contact details for any person (apart from yourself) responsible for ensuring their availability. In particular, you should name the person responsible for you if your work requires access to the Department research area. The signatures of these people should also be present on the project cover sheet before submission.

What qualifies as a critical resource?

In some cases a project may need to use data or build on algorithms described in a technical report or other document known to exist but not immediately available in Cambridge. In this case, this must be considered critical even if work could start without the report or data.

Using any hardware or software other than that available through a normal student account on UIS equipment (e.g. MCS) is considered non-standard. This includes personal machines, other workstations (e.g. research machines in the Department), FPGA boards, or even Raspberry Pis if they belong to someone else. Likewise, use of software written or owned by someone else that is not freely available as open-source will be considered as non-standard and should be declared.

Additional MCS Resources

It is reasonable to suppose that disk space and machine time will be made available in amounts adequate for all but extreme projects. Those who consider they may need more should provide a reasoned estimate of the resources required in the project proposal in consultation with the Supervisor. Additional file space should be requested through a web form , noting that:

• you should state in your application that you are Part II CST;
• requests for small increases of MCS space will need a very brief justification: please don't send your proposal;
• requests for substantial increases should also be accompanied by a brief supporting email to [email protected] from your Supervisor.

Note that some MATLAB toolkits are not available on the MCS but might be available on Department accounts.

Use of your own computer

If you are using your own computer, please state its specifications and also state your contingency plan in case it should fail (such as using MCS or another personal computer). Please also state your file backup plan and the revision control system you plan to use. If using your own computer please include the following text in your declaration:

I accept full responsibility for this machine and I have made contingency plans to protect myself against hardware and/or software failure.

Department Accounts

Access to Departmental computers can be granted if there is a good reason, e.g. 

• collaboration with a particular research group; 
• use of software not available on the MCS facility. 

If you plan to use a Department account then state this and explain why it is needed in your resources declaration. If relevant, the signature of a sponsoring member of the department (e.g. the owner of the specific resource) is required as an extra signature on the project cover sheet. In addition, your Supervisor should send an email to [email protected] requesting the account with a brief justification. 

Some Department resources and the people who can authorise their use: 

• Requests for resources involving a Department research machine should be authorised by a Lecturer, Reader or Professor who is in charge of managing the equipment. 

Access to the Department can be granted if there is a good reason. If you require access to the secure part of the William Gates Building, you should state who will be responsible for you whilst you are on the premises. They should sign your Project Proposal Coversheet as a Special Resource Sponsor. 

Third-Party Resources

Resources provided by your College, other University departments or industrial collaborators must be declared. The name and contact details (including email address) of the person in charge of the resource must be stated and their signature must be present on the project cover sheet. Resources from third parties can sometimes disappear unexpectedly, so please state why you believe this is not going to happen or else state your contingency plan in case it does.

In the case of projects that rely on support from outside the University it will be necessary to procure a letter from the sponsors that confirms both that their equipment will remain available right up to the end of the academic year and that they understand that the results of work done by students cannot be viewed as secret or proprietary.

You should bear in mind that the Examiners will require electronic submission of your dissertation and code. Therefore, you should not sign anything, such as a non-disclosure agreement, that would prevent you from submitting them.

Working with human participants

If your project involves collection of data via surveys, interviews or online, release of instrumented software, fieldwork, or experiments with human participants, such as usability trials or asking people to evaluate some aspect of your work, then you must seek approval by submitting a human participants request to the departmental Ethics Committee and record that you are going to do this, by ticking the appropriate box on your cover sheet.  This must occur before any of these activities start. Please read the Department's ethics policy .

Your project Supervisor will help you to fill in an online form ( read-only version ) containing two questions:

• A brief description of the study you plan to do;
• The precautions you will take to avoid any risk.

Simple guidance related to the most common types of study is available on the School of Technology Research Guidance site .  You may also find it useful to discuss your plans with the person supervising you for the Part II HCI course.

After submitting the ethics review form, you will receive feedback from the Ethics Committee within a few days. You must not start any study involving human participants without approval from the Ethics Committee.

Planning the project

As part of the project proposal, you should provide a detailed description of the work that needs to be performed, broken down into manageable chunks.  You will need to identify the key components that will go to make up your final product.  Credit is awarded specifically for showing a professional approach using any relevant management or software engineering methods at all stages of project design, development and testing. Plan an order in which you intend to implement the project components, arranging that both the list of tasks and the implementation order provide you with a sequence of points in the project where you can assess progress. Without a set of milestones it is difficult to pace your work so that the project as a whole gets completed on time.

When you have decomposed your entire project into sub-tasks you can try to identify which of these sub-tasks are going to be hard and which easy, and hence estimate the relative amounts of effort involved in each. These estimates, together with the known date when the dissertation must be submitted, should allow you to prepare a rough timetable for the work. The timetable should clearly make allowance for lecture loads, unit-of-assessment coursework, vacations, revision and writing your dissertation. Looking at the details of such a plan can give you insight into the feasibility of the project.  Ideally you should plan to start writing the dissertation at least six weeks before the submission date.

Languages and tools

It will also be necessary to make decisions about operating systems, programming languages, tools and libraries. In many cases there will be nothing to decide, in that the essence of the project forces issues. However, where you do have a choice, then take care to balance out the pros and cons of each option.  It is expected that students will be prepared to learn a new language or operating system if that is a natural consequence of the project they select.

Uncommon languages or ones where the implementation is of unknown reliability are not ruled out, but must be treated with care and (if at all possible) fall-back arrangements must be made in case insuperable problems are encountered.

Risk management

Projects are planned at the start of the year, and consequently it can be hard to predict the results of decisions that are made; thus any project proposal involves a degree of risk. Controlling and managing that risk is one of the skills involved in bringing a project to a successful conclusion. It is clear where to start: you should identify the main problem areas early and either allow extra margins of time for coping with them or plan the project so that there are alternative ways of solving key problems. A good example of this latter approach arises if a complete project requires a solution to a sub-problem X and a good solution to X would involve some complicated coding. Then a fall-back position where the project can be completed using a naive (possibly seriously inefficient, but nevertheless workable) solution to X can guard against the risk of you being unable to complete and debug the complicated code within the time limits.

Planning the write-up

As well as balancing your risks, you should also try to plan your work so that writing it up will be easy and will lead to a dissertation in which you can display breadth as well as depth in your understanding. This often goes hand-in-hand with a project structure which is clearly split into sub-tasks, which is, of course, also what you wanted in order that your management of your work on the project could be effective.

A good dissertation will be built around a varied portfolio of code samples, example output, tables of results and other evidence of the project’s successful completion. Planning this evidence right from the start and adjusting the project specification to make documenting it easier can save you a lot of agony later on.

Preparing the Project Proposal and consulting Project Checkers

You should keep in touch with both your Project Checkers from the briefing session until the final draft of your project proposal, making sure that they know what state your planning is in and that they have had a chance to read and comment on your ideas. Project Checkers will generally be reluctant to turn down a project outright, but if you feel that yours sound particularly luke-warm about some particular idea or aspect of what you propose you would do well to think hard (and discuss the issues with your Supervisor) before proceeding. If Project Checkers declare a project plan to be unacceptable, or suggest that they will only accept subject to certain conditions, rapid rearrangement of plans may be called for.

Dealings with your Project Checkers divide into three phases between the briefing session and submitting your proposal. Most of the communications will be best arranged by Moodle comments in the feedback box and all submissions of work are on Moodle.  Please be sure to take note of the various deadlines .

Phase 1 report: Selecting a topic

You start by preparing a Phase 1 report which, for 23/24 must be submitted on or before the first day of Michaelmas Full Term in October  Please pay careful attention to the points raised in the briefing lectures regarding selection of an appropriate topic. You must certainly choose something that has a defined and achievable success criterion. Note also that the marking scheme explicitly mentions preparation and evaluation, so please select something that will require a corresponding initial research/study phase and a corresponding (preferably systematic) evaluation phase.

You should complete a copy of the “Phase 1 Project Selection Status Report” and upload it to Moodle .

Phase 2: Full proposal draft: Filling out details

The details will include:

• Writing a description, running to a few hundred words.
• Devising a timetable, dividing the project into about 10 work packages each taking about a fortnight of your effort. The first couple of these might be preparatory work and the last three writing your dissertation, with the practical work in the middle. These should be identifiable deliverables and deadlines leading to submission of your dissertation at the beginning of the Easter Term. You will probably write your progress report as part of the fifth work package.
• Determining special resources and checking their availability.
• Securing the services of a suitable Supervisor.

Send all this to your Project Checkers and ask them to check the details. 

Phase 3: Final proposal

In the light of your Project Checkers’ comments, produce a final copy in PDF format. 

You do not secure signatures from your Project Checkers at this stage. Simply submit the proposal. 

Shortly after submission the Project Checkers will check your proposal again and, assuming that the foregoing steps have been followed carefully, all should be well and they will sign the proposal to signify formal acceptance. If the proposal is not acceptable you will be summoned for an interview.

Submission and Content of the Project Proposal

Completed project proposals must be submitted via Moodle by noon on the relevant day.

Format of the proposal

A project proposal is expected to up to 1000 words long. It consists of the following:

• A standard cover sheet
• The body of the proposal (see below).

When emailing drafts of your proposal to Project Checkers, please make sure they contain all of the information required on the final cover sheet.

The body of the proposal should incorporate:

• An introduction and description of the work to be undertaken.
• A statement of the starting point.
• Description of the substance and structure of the project: key concepts, major work items, their relations and relative importance, data structures and algorithms.
• A criterion that can later be used to determine whether the project has been a success.
• Plan of work, specifying a timetable and milestones.
• Resource declaration.

Introduction and description

This text will expand on the title quoted for your project by giving further explanation both of the background to the work you propose to do and of the objectives you expect to achieve. Quite often a project title will do little more than identify a broad area within which you will work: the accompanying description must elaborate on this, giving details of specific goals to be achieved and precise characterisations of the methods that will be used in the process. You should identify the main sub-tasks that make up your complete project and outline the algorithms or techniques to be adopted in completing them. A project description should give criteria that can be used at the end of the year to test whether you have achieved your goals, and should back this up by explaining what form of evidence to this effect you expect to be able to include in your dissertation.

Starting point

A statement of the starting point must be present to ensure that all candidates are judged on the same basis. It should record any significant bodies of code or other material that will form a basis for your project and which exist at project proposal time. Provided a proper declaration is made here, it is in order to build your final project on work you started perhaps even a year earlier, or to create parts of your programs by modifying existing ones written by somebody else. Clearly the larger the input to your project from such sources the more precise and detailed you will have to be in reporting just what baseline you will be starting from. The Examiners will want this section to be such that they can judge all candidates on the basis of that part of work done between project proposal time and the time when dissertations are submitted. The starting point should describe the state of existing software at the point you write your proposal (so work that you may have performed over the summer vacation is counted as preparatory work).

Success criterion

Similarly, a proposal must specify what it means for the project to be a success. It is unacceptable to say “I’ll just keep writing code in this general area and what I deliver is what you get”. It is advisable to choose a reasonably modest, but verifiable, success criterion which you are as certain as possible can be met; this means that your dissertation can claim your project not only satisfies the success criterion but potentially exceeds it. Projects that do not satisfy the success criterion are, as in real life, liable to be seen as failures to some extent.

You will need to describe how your project is split up into two- or three-week chunks of work and milestones, as explained in the planning section .

Resource declaration

You should list resources required, as described in the resources section .

Failure to submit a project proposal on time

Any student who fails to submit a project proposal on time is in breach of a Regulation and will no longer be regarded as a Candidate for Part II of the Computer Science Tripos. The Chairman of Examiners will write to the appropriate Senior Tutor as follows:

Dear Senior Tutor,

XXX has failed to submit a project proposal for Part II of the Computer Science Tripos.  The Head of Department was therefore unable to approve the title by the deadline specified in Regulation 17 for the Computer Science Tripos [Ordinances 2005, p268,amended by Notices (Reporter, 2010-11, pp.94 and 352, http://www.admin.cam.ac.uk/univ/so/2011/chapter04-section9.html#heading2-43 )].  XXX is therefore in breach of the regulation and is thus no longer eligible to be a Candidate for Part II of the Computer Science Tripos.  Please could you take appropriate action. I am copying this  letter to the Secretary of the Applications Committee of the Council.

Yours sincerely,

------------------------- Chair of the Examiners Department of Computer Science and Technology William Gates Building JJ Thomson Avenue Cambridge, CB3 0FD

Department of Computer Science and Technology University of Cambridge William Gates Building 15 JJ Thomson Avenue Cambridge CB3 0FD

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How to Write a Proposal for a Computer Science Topic

Kevin blankinship.

Writing a topic proposal represents a major part of computer-science projects in high school, college and graduate school. When you develop an idea for your university capstone project or master's thesis, you'll be required to submit a topic proposal to your professors. Jobs in academic and industrial fields require such proposals when pitching new projects. Learning how to write a thorough and concise topic proposal is a life skill that you will be called upon to use throughout your career.

Explore this article

• Write an introduction
• Clarify the specific problem or concern
• Record your research methods
• Cite your sources in a bibliography

things needed

• Word processing software

1 Write an introduction

Write an introduction. This should include an overview of the concepts, terms and issues involved with your project. Place your project in the greater context of computer science or mathematics by starting with a more general scope, then zeroing in on more specific concerns related to your topic. For a project involving a more efficient database algorithm, for example, start off with an overview of how such algorithms work in general.

2 Clarify the specific problem or concern

Clarify the specific problem or concern that your project will address. The goal of computer science projects, as with any original research, is to identify an area of the field which has been ignored or understudied, and then contribute a solution to that problem. Include a brief literature review outlining the work which has been done previously, then show that your project will contribute an original solution by explaining how the project resolves a previously unaddressed problem. Present your solution in a concise research statement, which will guide the rest of your proposal.

3 Record your research methods

Record your research methods. Provide details of the algorithms and program logic you plan on using. Include a timeline and budget, if necessary, for your project. For short-term class projects, allow two to three months for completion. Give yourself six months to a year for longer projects, such as a capstone project or master's thesis.

4 Cite your sources in a bibliography

Cite your sources in a bibliography. Include all sources used in formulating your literature review at the beginning of the proposal. Use American Psychological Association (APA) style, which is the preferred citation format for computer science, as well as the hard sciences and engineering.

• Avoid plagiarism. When in doubt, cite a source. Also, invest the time it takes to be sure that your work is original. Read other project proposals and reports to be sure that you're making an original contribution to the field.
• 1 University of Illinois at Urbana-Champagne: Writing a Research Proposal
• 2 Harold B. Lee Library, Brigham Young University: Computer Science -- Literary Styles and Their Application

About the Author

Kevin Blankinship began writing professionally in 2010. His work is featured online, focusing on business, technology, physical fitness, education and religion. Blankinship holds a bachelor's and a master's degree in comparative literature and is pursuing a doctorate in Arabic language and literature from the University of Chicago.

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Examples of research proposals

How to write your research proposal, with examples of good proposals.

Research proposals

Your research proposal is a key part of your application. It tells us about the question you want to answer through your research. It is a chance for you to show your knowledge of the subject area and tell us about the methods you want to use.

We use your research proposal to match you with a supervisor or team of supervisors.

In your proposal, please tell us if you have an interest in the work of a specific academic at York St John. You can get in touch with this academic to discuss your proposal. You can also speak to one of our Research Leads. There is a list of our Research Leads on the Apply page.

When you write your proposal you need to:

• Highlight how it is original or significant
• Explain how it will develop or challenge current knowledge of your subject
• Identify the importance of your research
• Show why you are the right person to do this research
• Research Proposal Example 1 (DOC, 49kB)
• Research Proposal Example 2 (DOC, 0.9MB)
• Research Proposal Example 3 (DOC, 55.5kB)
• Research Proposal Example 4 (DOC, 49.5kB)

Subject specific guidance

• Writing a Humanities PhD Proposal (PDF, 0.1MB)
• Writing a Creative Writing PhD Proposal (PDF, 0.1MB)
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How to Write a Research Proposal in Google Docs

Ever wonder “ why ” something happens or “ how ” to fix a problem that keeps popping up? Maybe you have a wild “what if” idea. Those are awesome questions that can lead to amazing discoveries! A research proposal is your key to unlocking those answers and making a real difference.

Think of it like this: You’re a detective on a case. Google Docs is your super cool notepad to plan your investigation. This guide will show you how to use it, step-by-step, to write a killer research proposal.

Here’s what you’ll learn:

• How to grab people’s attention with a super interesting intro (think detective laying out the clues!)
• What to write in each part of your proposal (no need for fancy research jargon here!)
• How to use Google Docs’ awesome features to keep yourself organized and work with others (sharing is caring!).

So, put on your detective hat, open Google Docs, and let’s turn your question into a mind-blowing research proposal!

Table of Content

What is a Research Proposal?

Ph.d. research proposals, free research proposal templates by google docs, tips for a strong research proposal, faqs – writing a research proposal in google docs.

A research proposal is a blueprint for a research project . Writing a research proposal involves outlining your objectives , methodology, and expected outcomes . A research proposal includes Research Questions, background and significance, Literature review, methodology, Timeline, budget, and Expected outcomes. The purpose of making a research proposal is to convince others of the importance of the project. By using Google Docs , you can easily make any research proposal while collaborating with others.

• Your proposal should contain at least the following elements in the list of Ph.D. Research Proposal,
• A provisional title for the Proposal.
• A key question, hypothesis, or broad topic for further investigation.
• An outline of the key aims of the research proposal.
• A brief outline of key literature in the area.

Google Docs is a platform where it provides a variety of free templates in many fields. Anybody can check and edit the templates according to their need. Here are some of the free templates provided below for your reference where you can edit accordingly and make your research proposal differently.

Research Proposal Free Template 1

To Download this template Click here

Research Proposal Free Template 2

To Download this template Click Here

Research Proposal Free Template 3

Writing a research proposal in Google Docs is an easy process. Here’s a step-by-step guide:

Step 1: Sign in to Google Docs

Go to Google Docs and sign in to your Google account. If you don’t have one, you’ll need to create one.

Step 2: Create a New Document

Click on the “+” (New) button to create a new document. You can choose a blank document or use a template if available.

Step 3: Set Up Your Document

Set up your document according to the guidelines provided by your institution or the requirements of your research proposal. This includes formatting your title page, adding headings, and adjusting margins.

Step 4: Structure Your Proposal

Use headings (e.g., Introduction, Literature Review, Methodology) to organize your proposal into sections. You can easily add headings by selecting the text and choosing a heading style from the toolbar.

Step 5: Write Your Proposal

Start writing your proposal under each section heading. Google Docs provides a familiar word processing environment with basic formatting tools like bold, italic, and bullet points.

Step 6: Collaborate with Others

If you’re working with collaborators or seeking feedback, you can easily share your document with others. Click on the “Share” button in the top right corner, enter the email addresses of the people you want to share with and choose their permission level (e.g., edit, comment, view).

Step 7: Review and Edit

Once you’ve completed your proposal, review it carefully for any errors or inconsistencies. You can use the spelling and grammar check tools to help you proofread your document.

Step 8: Finalize Your Proposal

Make any final adjustments and ensure that your proposal meets all the requirements. Double-check formatting, headings, and citations before submitting.

Step 9: Save and Share

Google Docs automatically saves your work as you go, but it’s a good idea to save a final copy to your Google Drive or download it as a PDF or Word document. Share the final version with your advisor or colleagues as needed.

By following these steps, you can effectively write a research proposal using Google Docs and take advantage of its collaboration features and convenience.

• Put a precise objective and research question that your proposal aims to address.
• Ensure your proposal addresses a significant problem or gap in knowledge within your field.
• Conduct a Literature Review of existing research to provide context and support for your proposal.
• Outline the methods and techniques clearly that you’ll use to conduct your research.
• Highlight the potential impact and contributions of your research.
• Provide a realistic timeline for completing your project work.
• Clearly outline the budget needed for your research.

Writing a research proposal in Google Docs offers many benefits for researchers. The platform’s collaborative features enable teamwork, allowing multiple contributors to edit and provide feedback in real time. Google Docs’ auto-save function ensures that your work is constantly backed up, reducing the risk of data loss. By learning Google Docs’ features, researchers can turn up the proposal writing process and enhance their productivity. Therefore, utilizing Google Docs for making research proposals is an efficient and effective approach, motivating researchers to collaborate, create, and communicate their ideas easily.

Does Google Docs have a proposal template?

Yes, Google Docs offers many proposal templates. Open Google Docs, click on the Template Gallery, and search for “proposal” and you’ll get to see many of them. Then, select a template that suits your needs and customize it for your research proposal.

How do I write a research proposal for Google?

Use Google Docs to create a new document, structure it with sections such as Introduction, Literature Review, Methodology, Results, Conclusion, and References, and collaborate with others by sharing the document.

What is the format of writing a research proposal?

The format of writing a research proposal typically includes, Title Page Abstract Introduction Literature Review Methodology Results and Discussion Conclusion References

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Dear Colleague Letter: Planning Proposals for Centers of Research Excellence in Science and Technology (CREST Centers) in Computer and Information Science and Engineering (CISE)

May 09, 2024

Dear Colleagues:

Consistent with the National Science Foundation's (NSF) efforts to increase institutional diversity within science, technology, engineering and mathematics (STEM), the Directorate of STEM Education (EDU) and the Directorate for Computer and Information Science and Engineering (CISE) jointly encourage the submission of planning proposals for a future CREST center proposal with a focus on research in all areas of CISE to include the participation of the full spectrum of diverse talent in STEM.

The CREST Program supports the creation of research centers that will lead to strong societal impacts through 5-year awards. The projects focus on the enhancement of institutional capacity building and research expansion at Minority-Serving Institutions (MSIs) through the establishment of centers that effectively integrate education and research. CREST Center awards promote the development of new knowledge, the increase in the research productivity of individual faculty, institution, and the expanded engagement of students from all backgrounds in STEM disciplines

A CREST award is expected to catalyze institutional transformation through the development of research capabilities aligned with the institution’s mission and long-term goals. Demonstrated leadership to increase opportunities everywhere, for everyone in STEM is expected at all levels – students, postdoctoral researchers when applicable, and faculty. The research activities supported by CREST are expected to enable the full participation of faculty, graduate students, and undergraduates in a nationally competitive research enterprise.

A competitive CREST proposal will include a meaningful, coherent plan for building sustainable research capability. Formulation of such a plan requires time and resources, which may not otherwise be available to some and thus could constitute a barrier to preparing a CREST proposal. Through this Dear Colleague Letter (DCL), EDU and CISE jointly encourage the submission of planning proposals for CREST centers with a focus on all core research areas within CISE, to help mitigate potential barriers to the preparation of competitive CREST proposals for the proposing institutions and Principal Investigators (PIs).

A CREST center proposal planning award could be used to support initial conceptualization and design of collaborative activities to facilitate the formulation of new and coherent plans for future submission of a CREST center proposal. Anticipated planning activities could include, but are not limited to, planning visits/meetings within the institution and with partnering institutions to discuss potential collaborations, exchanges to launch/initiate scientific collaboration, strategic planning (including the development of a collaborative research plan), training efforts and infrastructure needs to enable coordination of collaborative efforts, and development of evaluation strategies.

Institutions from EPSCoR jurisdictions are always encouraged to apply for NSF support and are particularly welcome to apply to the CREST program. In addition, we seek individuals from EPSCoR jurisdictions to serve as merit review panelists which is an excellent way to learn about an NSF program you may want to apply to in the future.

PROPOSAL PREPARATION AND SUBMISSION

Proposals must be prepared in accordance with the guidance for Planning Proposals specified in Chapter II.F.1 of the NSF Proposal and Award Policies and Procedures Guide (PAPPG) and submitted through Research.gov. Proposers should select the current PAPPG as the funding opportunity and direct proposals to EDU/EES/Centers for Research Excellence in S&T, as listed in research.gov.

Interested proposers should follow this guidance closely:

• The proposal must include a clear statement as to why this project is appropriate for a planning proposal, including how the funds will be used to formulate a sound approach for future submission of a CREST center proposal.
• The proposal must explain how a competitive research center will be created and sustained.
• The proposed research should be aligned with research supported by the Directorate for l Computer and Information Science and Engineering (CISE). The PIs are encouraged to outline a vision that simultaneously promotes inclusiveness and research excellence in CISE focused funding areas.
• The PI must hold a faculty appointment at an eligible MSI that awards degrees in computer science or computer engineering and must be eligible to submit a future CREST center proposal as defined in the recent CREST Centers solicitation .
• The budget may be up to $100,000/year (including indirect costs) and up to two years in duration.

Prospective PIs must send an initial concept outline (no more than one page) by email no later than August 1, 2024 , to one of the Program Officers listed below to verify that the proposal topic fits with the research areas of the Directorate for Computer and Information Sciences and Engineering. An invitation from at least one NSF Program Officer to submit a full planning proposal must be uploaded by the PI in the “Program Officer Concurrence Email" section in Research.gov at submission of planning proposal. Planning proposals submitted in response to this DCL for consideration in FY 2025 are welcome through October 1, 2024, but earlier submission is strongly encouraged.

Please contact the following Program Officers for concept outline submission or any questions regarding this DCL:

• Subrata Acharya, Lead PO CISE Research Expansion, (CISE/CCF) [email protected]
• Sonal Dekhane, PO EDU, (EDU/EES), [email protected]
• Tomasz Durakiewicz, PO EDU, (EDU/EES, MPS/DMR), [email protected]
• Luis Cubano, Acting Deputy Division Director, EDU, (EDU/EES), [email protected]

James L. Moore III Assistant Director Directorate for STEM Education (EDU) Dilma Da Silva Acting Assistant Director Directorate for Computer & Information Science & Engineering (CISE)