computer science phd topics 2020

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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PhD in Computer Science

The PhD in Computer Science is a small and selective program at Pace University that aims to cultivate advanced computing research scholars and professionals who will excel in both industry and academia. By enrolling in this program, you will be on your way to joining a select group at the very nexus of technological thought and application.

Learn more about the PhD in Computer Science .

Forms and Research Areas

General forms.

• PhD Policies and Procedures Manual – The manual contains all the information you need before, during, and toward the end of your studies in the PhD program.
• Advisor Approval Form (PDF) – Completed by student and approved by faculty member agreeing to the role as advisor.
• Committee Member Approval Form (PDF) – Completed by student with signatures of each faculty member agreeing to be on dissertation committee.
• Change in Advisor or Committee Member Approval Form (PDF) – Completed by student with the approval of new advisor or committee member. Department Chair approval needed.
• Qualifying Exam Approval Form (PDF) – Complete and return form to the Program Coordinator no later than Week 6 of the semester.

Dissertation Proposal of Defense Forms

• Application for the Dissertation Proposal of Defense Form (PDF) – Completed by student with the approval of committee members that dissertation proposal is sufficient to defend. Completed form and abstract and submitted to program coordinator for scheduling of defense.
• Dissertation Proposal Defense Evaluation Form (PDF) – To be completed by committee members after student has defended his dissertation proposal.

Final Dissertation Defense Forms

• Dissertation Pre- Defense Approval Form (PDF) – Committee approval certifying that the dissertation is sufficiently developed for a defense.
• Dissertation Defense Evaluation Form (PDF) – Completed by committee members after student has defended his dissertation.

All completed forms submitted to the program coordinator.

Research Areas

The Seidenberg School’s PhD in Computer Science covers a wealth of research areas. We pride ourselves on engaging with every opportunity the computer science field presents. Check out some of our specialties below for examples of just some of the topics we cover at Seidenberg. If you have a particular field of study you are interested in that is not listed below, just get in touch with us and we can discuss opportunities and prospects.

Some of the research areas you can explore at Seidenberg include:

Algorithms And Distributed Computing

Algorithms research in Distributed Computing contributes to a myriad of applications, such as Cloud Computing, Grid Computing, Distributed Databases, Cellular Networks, Wireless Networks, Wearable Monitoring Systems, and many others. Being traditionally a topic of theoretical interest, with the advent of new technologies and the accumulation of massive volumes of data to analyze, theoretical and experimental research on efficient algorithms has become of paramount importance. Accordingly, many forefront technology companies base 80-90% of their software-developer hiring processes on foundational algorithms questions. The Seidenberg faculty has internationally recognized strength in algorithms research for Ad-hoc Wireless Networks embedded in IoT Systems, Mobile Networks, Sensor Networks, Crowd Computing, Cloud Computing, and other related areas. Collaborations on these topics include prestigious research institutions world-wide.

Machine Learning In Medical Image Analysis

Machine learning in medical imaging is a potentially disruptive technology. Deep learning, especially convolutional neural networks (CNN), have been successfully applied in many aspects of medical image analysis, including disease severity classification, region of interest detection, segmentation, registration, disease progression prediction, and other tasks. The Seidenberg School maintains a research track on applying cutting-edge machine learning methods to assist medical image analysis and clinical data fusion. The purpose is to develop computer-aided and decision-supporting systems for medical research and applications.

Pattern recognition, artificial intelligence, data mining, intelligent agents, computer vision, and data mining are topics that are all incorporated into the field of robotics. The Seidenberg School has a robust robotics program that combines these topics in a meaningful program which provides students with a solid foundation in the robotics sphere and allows for specialization into deeper research areas.

Cybersecurity

The Seidenberg School has an excellent track record when it comes to cybersecurity research. We lead the nation in web security, developing secure web applications, and research into cloud security and trust. Since 2004, Seidenberg has been designated a Center of Academic Excellence in Information Assurance Education three times by the National Security Agency and the Department of Homeland Security and is now a Center of Academic Excellence in Cyber Defense Education. We also secured more than $2,000,000 in federal and private funding for cybersecurity research during the past few years.

Pattern Recognition And Machine Learning

Just as humans take actions based on their sensory input, pattern recognition and machine learning systems operate on raw data and take actions based on the categories of the patterns. These systems can be developed from labeled training data (supervised learning) or from unlabeled training data (unsupervised learning). Pattern recognition and machine learning technology is used in diverse application areas such as optical character recognition, speech recognition, and biometrics. The Seidenberg faculty has recognized strengths in many areas of pattern recognition and machine learning, particularly handwriting recognition and pen computing, speech and medical applications, and applications that combine human and machine capabilities.

A popular application of pattern recognition and machine learning in recent years has been in the area of biometrics. Biometrics is the science and technology of measuring and statistically analyzing human physiological and behavioral characteristics. The physiological characteristics include face recognition, DNA, fingerprint, and iris recognition, while the behavioral characteristics include typing dynamics, gait, and voice. The Seidenberg faculty has nationally recognized strength in biometrics, particularly behavioral biometrics dealing with humans interacting with computers and smartphones.

Big Data Analytics

The term “Big Data” is used for data so large and complex that it becomes difficult to process using traditional structured data processing technology. Big data analytics is the science that enables organizations to analyze a mixture of structured, semi-structured, and unstructured data in search of valuable information and insights. The data come from many areas, including meteorology, genomics, environmental research, and the internet. This science uses many machine learning algorithms and the challenges include data capture, search, storage, analysis, and visualization.

Business Process Modeling

Business Process Modeling is the emerging technology for automating the execution and integration of business processes. The BPMN-based business process modeling enables precise modeling and optimization of business processes, and BPEL-based automatic business execution enables effective computing service and business integration and effective auditing. Seidenberg was among the first in the nation to introduce BPM into curricula and research.

Educational Approaches Using Emerging Computing Technologies

The traditional classroom setting doesn’t suit everyone, which is why many teachers and students are choosing to use the web to teach, study, and learn. Pace University offers online bachelor's degrees through NACTEL and Pace Online, and many classes at the Seidenberg School and Pace University as a whole are available to students online.

The Seidenberg School’s research into new educational approaches include innovative spiral education models, portable Seidenberg labs based on cloud computing and computing virtualization with which students can work in personal enterprise IT environment anytime anywhere, and creating new semantic tools for personalized cyber-learning.

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Phd program, find your passion for research.

Duke Computer Science gives incoming students an opportunity to investigate a range of topics, research problems, and research groups before committing to an advisor in the first year. Funding from the department and Duke makes it possible to attend group meetings, seminars, classes and colloquia. Students may work on multiple problems simultaneously while finding the topic that will motivate them through their first project. Sharing this time of learning and investigation with others in the cohort helps create lasting collaborators and friends.

Write a research proposal the first year and finish the research the second under the supervision of the chosen advisor and committee; present the research results to the committee and peers. Many students turn their RIP work into a conference paper and travel to present it.

Course work requirements are written to support the department's research philosophy. Pass up to four of the required six courses in the first two years to give time and space for immersing oneself in the chosen area.

Years three through five continue as the students go deeper and deeper into a research area and their intellectual community broadens to include collaborators from around the world. Starting in year three, the advisor funds the student's work, usually through research grants. The Preliminary exam that year is the opportunity for the student to present their research to date, to share work done by others on the topic, and to get feedback and direction for the Ph.D. from the committee, other faculty, and peers.

Most Ph.D students defend in years five and six. While Duke and the department guarantee funding through the fifth year, advisors and the department work with students to continue support for work that takes longer.

Teaching is a vital part of the Ph.D. experience. Students are required to TA for two semesters, although faculty are ready to work with students who want more involvement. The Graduate School's Certificate in College Teaching offers coursework, peer review, and evaluation of a teaching portfolio for those who want to teach. In addition, the Department awards a Certificates of Distinction in Teaching for graduating PhD students who have demonstrated excellence in and commitment to teaching and mentoring.

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Computer Science Ph.D. Program

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The Cornell Ph.D. program in computer science is consistently ranked among the top six departments in the country, with world-class research covering all of computer science. Our computer science program is distinguished by the excellence of the faculty, by a long tradition of pioneering research, and by the breadth of its Ph.D. program. Faculty and Ph.D. students are located both in Ithaca and in New York City at the Cornell Tech campus . The Field of Computer Science also includes faculty members from other departments (Electrical Engineering, Information Science, Applied Math, Mathematics, Operations Research and Industrial Engineering, Mechanical and Aerospace Engineering, Computational Biology, and Architecture) who can supervise a student's Ph.D. thesis research in computer science.

Over the past years we've increased our strength in areas such as artificial intelligence, computer graphics, systems, security, machine learning, and digital libraries, while maintaining our depth in traditional areas such as theory, programming languages and scientific computing.  You can find out more about our research here . 

The department provides an exceptionally open and friendly atmosphere that encourages the sharing of ideas across all areas. 

Cornell is located in the heart of the Finger Lakes region. This beautiful area provides many opportunities for recreational activities such as sailing, windsurfing, canoeing, kayaking, both downhill and cross-country skiing, ice skating, rock climbing, hiking, camping, and brewery/cider/wine-tasting. In fact, Cornell offers courses in all of these activities.

The Cornell Tech campus in New York City is located on Roosevelt Island.  Cornell Tech  is a graduate school conceived and implemented expressly to integrate the study of technology with business, law, and design. There are now over a half-dozen masters programs on offer as well as doctoral studies.

FAQ with more information about the two campuses .

Ph.D. Program Structure

Each year, about 30-40 new Ph.D. students join the department. During the first two semesters, students become familiar with the faculty members and their areas of research by taking graduate courses, attending research seminars, and participating in research projects. By the end of the first year, each student selects a specific area and forms a committee based on the student's research interests. This “Special Committee” of three or more faculty members will guide the student through to a Ph.D. dissertation. Ph.D. students that decide to work with a faculty member based at Cornell Tech typically move to New York City after a year in Ithaca.

The Field believes that certain areas are so fundamental to Computer Science that all students should be competent in them. Ph.D. candidates are expected to demonstrate competency in four areas of computer science at the high undergraduate level: theory, programming languages, systems, and artificial intelligence.

Each student then focuses on a specific topic of research and begins a preliminary investigation of that topic. The initial results are presented during a comprehensive oral evaluation, which is administered by the members of the student's Special Committee. The objective of this examination, usually taken in the third year, is to evaluate a student's ability to undertake original research at the Ph.D. level.

The final oral examination, a public defense of the dissertation, is taken before the Special Committee.

To encourage students to explore areas other than Computer Science, the department requires that students complete an outside minor. Cornell offers almost 90 fields from which a minor can be chosen. Some students elect to minor in related fields such as Applied Mathematics, Information Science, Electrical Engineering, or Operations Research. Others use this opportunity to pursue interests as diverse as Music, Theater, Psychology, Women's Studies, Philosophy, and Finance.

The computer science Ph.D. program complies with the requirements of the Cornell Graduate School , which include requirements on residency, minimum grades, examinations, and dissertation.

The Department also administers a very small 2-year Master of Science program (with thesis). Students in this program serve as teaching assistants and receive full tuition plus a stipend for their services.

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PhD Program

We are proud of the quality of PhD students we attract and the training they receive. All of our students receive support, including an annual stipend, in the form of external and internal competitive fellowships, research fellowships, or teaching fellowships. As a PhD candidate, you will share in the excitement of discovery as you collaborate with our faculty on cutting-edge research . You will also acquire strong independent research skills and begin to develop your own reputation as a member of the research community.

Because the advisor-graduate relationship is the cornerstone of a successful PhD experience, all new PhD candidates are carefully matched with faculty advisors based on mutual research interests. In addition, an active three-person PhD committee is created for each PhD student to provide cogent advice throughout your degree program.

You will find the work here challenging and personally rewarding. Students who complete our PhD program are well-prepared for careers in academia, research, government, and industry. Please visit the Graduate Admissions information page  for application requirements, deadlines, and other important information.

Application Deadlines:

• The PhD deadline for fall is December 15th. (No recruiting for spring admissions.)
• The application will be available for submission on or around August 15.

To learn more about the PhD admissions process, please visit our PhD Admissions FAQ page .

Apply today

Learn more about the graduate admissions process and start your application.

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Computer Science at Brown University Providence, Rhode Island 02912 USA Phone: 401-863-7600 Map & Directions / Contact Us

Email forwarding for @cs.stanford.edu is changing. Updates and details here . CS Commencement Ceremony June 16, 2024.  Learn More .

PhD Admissions

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The Computer Science Department PhD program is a top-ranked research-oriented program, typically completed in 5-6 years. There are very few course requirements and the emphasis is on preparation for a career in Computer Science research. 

Eligibility

To be eligible for admission in a Stanford graduate program, applicants must meet:

• Applicants from institutions outside of the United States must hold the equivalent of a United States Bachelor's degree from a college or University of recognized good standing. See detailed information by region on  Stanford Graduate Admissions website. 
• Area of undergraduate study . While we do not require a specific undergraduate coursework, it is important that applicants have strong quantitative and analytical skills; a Bachelor's degree in Computer Science is not required.

Any questions about the admissions eligibility should be directed to  [email protected] .

Application Checklist

An completed online application must be submitted by the CS Department application deadline and can be found  here .

Application Deadlines

The online application can be found here  and we will only one admissions cycle for the PhD program per respective academic term.

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Doctoral Degree Program

The Ph.D. degree program in Computer Science provides for a rigorous foundation in theoretical and applied computer science. Students obtain in-depth knowledge by satisfying a breadth course requirement intended to ensure broad knowledge of computer sciences as well as satisfy a depth requirement in the ability to conduct research to advance knowledge and application of Computer Sciences to diverse fields.

Our program places a strong emphasis on research and on graduates making novel contributions to Computer Science in the form of a dissertation and scholarly publications. Students pursuing the Ph.D. degree are trained to become teachers, researchers, and technical leaders in industry, academia, or research labs.

Students will be prepared to be technical problem solvers, competent in the state of the art, and will master a particular aspect of Computer Science. They will be trained to identify and clearly formulate problems, to develop and analyze algorithmic solutions, and to direct research.

The Ph.D. in Computer Science is a traditional face-to-face/in-person program. There is currently no option to complete it fully online.

Fall 2021 - present

Fall 2020 - summer 2021, fall 2017 - summer 2020, prior to fall 2017, course requirements.

Students who began the Ph.D. program Fall 2021 through present : A student must complete a minimum of 66 credit hours subject to the following restrictions:

• Can include 1 hour of COSC 6110
• Can include up to 3 hours of COSC 6398 Special Problems if taken within the first four longer terms of the program¹
• Can include up to 6 hours of non-COSC graduate courses¹
• Can include up to 9 hours of transfer graduate coursework following university   Transfer Credit policy ¹
• At least 24 credit hours of Doctoral Research (COSC 8x98).
• At least 3, but not more than 12 credit hours of dissertation (COSC 8x99), to be taken in the term of anticipated graduation.
• ¹Requires prior approval from the director of graduate studies via a Graduate & Professional Student Petition

Research Advisor

Students are urged to find a research advisor as early as possible. Full-time and part-time students must declare a research advisor by the end of the first long term by completing the Research Advisor Assignment form . Student may enroll in doctoral research hours once they have declared a research advisor.

Graduate Colloquium/Research Methods

All PhD students are required to pass COSC 6110 - Graduate Colloquium or COSC 6321 - Research Methods in Computer Science by the end of the second long term in the program.

Research Competency Evaluation (RCE) Exam

Spring 2024 RCE Exam date is Friday, April 19, 2024

PhD students are expected to spend a substantial amount of time on research starting in the first semester. The RCE requires students to learn and demonstrate specific skills necessary to doing research early in their career. These include the ability to perform a literature review, understand and synthesize research topics, conduct independent and collaborative research to the standards of the chosen discipline, and communicate the findings in a scholarly fashion.

For the RCE exam, the student (in consultation with their research advisor) selects and conducts research on a topic, writes an ACM/IEEE style paper, and presents a talk, to be approved by the student’s RCE committee. A student may submit and present their own submitted or published research for RCE requirements. The RCE committee will evaluate the student with respect to two questions:

• Has the student demonstrated scholarship and potential to conduct original research?
• Has the student demonstrated the ability to communicate technical content effectively to a general computer science audience?

RCE Process

The student’s research advisor, with the consultation of the student’s RCE committee, assigns a research topic to the student no later than the beginning of the third long term. The student may also receive an initial selection of relevant literature. The project should be designed to test the student’s ability to independently conduct research at a level commensurate with his/her education.

The student will prepare a written report and an oral presentation of the project results. The research advisor may provide feedback during the preparation of the written report, but the report should be substantially the student’s own work. The report should include a statement of the problem, present a critical survey of relevant methods and existing state of the art solutions to the problem, present their own methods and proposed solution, results, and discussion. The length of the report should be similar to a standard paper in the research area and be formatted using the IEEE or ACM docx or Latex templates. The report should be submitted to the student’s RCE committee at least one week prior to the RCE exam. The RCE exam consists of a public presentation of the project, followed by a closed–door oral examination.

RCE Committee

The student’s RCE committee should comprise of at least 3 Computer Science faculty members (not including the research advisor) and approved by the Director of Graduate Studies. At least one committee member should be from outside the student's research area. The research project topic for the RCE will be selected in consultation with the student’s research advisor. Once the evaluation has taken place, the Chair of the RCE committee will inform the student and the Director of Graduate Studies about the outcome of the exam. Should a student switch their research advisor after completing the RCE requirement, the student is not required to retake the RCE exam.

RCE Deadlines

The RCE exam must be completed by the end of the third long term of the PhD program. It is not necessary to complete all coursework before attempting the RCE exam. The RCE Exam is offered once each long term. Unless otherwise announced, in Fall it is offered the second or third Friday in November and in Spring it is offered the second or third Friday of April. The exact date RCE date for each semester will be announced at the beginning of the respective semester.

RCE Outcome

The student will either pass or fail the RCE exam, and this decision, based on a majority vote of the committee, will be communicated within one (1) week following the exam. Once the student passes, they may proceed with the preparation of the dissertation proposal. If failed, the student may request a second attempt. For a second attempt, the RCE committee will assign additional work, which should be completed and presented at the end of the next long term. Students who fail the second attempt will be discontinued from the PhD program.

Breadth Requirements

A student satisfies the core requirement by taking a set of three or more courses from the lists below. At least one course must be from the Theory list and one from the Systems list. The remaining course may be from either list. In exceptional cases, the Director of Graduate Studies may transfer or substitute at most two of the three courses based on equivalent courses taken at another university following transfer or course substitution policies. Similarly, any breadth course taken as a MS Computer Science student at UH may count towards the breadth requirement following transfer and course substitution policies. Time limitations apply to transferred and substituted courses.

Breadth Requirements Completion Period

The “breadth requirement completion period” begins as soon as the student has successfully passed the RCE Exam. The breadth requirement completion period applies to PhD students as well as MS students who later pursue the PhD program.

Full-time students must complete the breadth requirements in at most two consecutive long terms after passing the RCE Exam. Part-time students (6 hours or less every term) must complete the breadth requirement in at most four consecutive long terms after passing the RCE Exam. Failure to complete this requirement within the specified timeframe normally results in an MS student not being allowed to continue into the PhD program and a PhD student being dismissed from the PhD program.

Students who began the Ph.D. program Fall 2020 through Summer 2021 : A student must complete a minimum of 66 credit hours subject to the following restrictions:

• Can include up to 3 hours of COSC 6398 Special Problems¹

Graduate Colloquium/Research Method Courses

All Ph.D. students are required to take either the Graduate Colloquium course (COSC 6110) or the Research Method course (COSC 6321) within the first two years of enrollment.

Students are urged to find a research advisor as early as possible. Full-time and part-time students must have a research advisor declared by the end of the second long term by completing the Research Advisor Assignment form . Student may enroll in doctoral research hours if they have an advisor and have completed the core requirement.

Core Completion Period

The “core completion period” begins as soon as the student has accumulated 18 or more hours of credits applicable to a graduate degree (M.S. or Ph.D.) in Computer Science. These include transferred credits, waived credits, and credits earned at UH. The core completion period applies to Ph.D. students as well as M.S. students who later pursue the Ph.D. program. Full-time students must complete the core requirements in at most 2 consecutive long terms after the start of the core completion period. Part-time students (6 hours or less every term) must complete the core requirement in at most 4 consecutive long terms after the start of the core completion period. Failure to complete the core requirements with the required GPA and within the specified timeframe normally results in an M.S. student not being allowed to continue into the Ph.D. program and a Ph.D.  student being dismissed from the Ph.D. program.

Core Requirements

A student satisfies the core requirement by taking at least 4 core courses with:

• At least 2 courses from the Theory list
• At least 2 courses from the Systems list
• At least 3.40 or higher core course GPA
• No grade less than B

The Director of Graduate Studies may in exceptional cases waive at most 2 of the 4 courses based on similar courses taken at another university. If a students takes Ph.D. core courses while enrolled as an M.S. student in the UH Department of Computer Science and is subsequently admitted to the UH Computer Science Ph.D. program, any Ph.D. core courses taken within 5 years prior to the date of the Ph.D. admissions can count towards meeting the Ph.D. core requirement.

Full-time students must complete the core requirements in at most 2 long semesters after core completion period begins. Part-time students (6 hours or less every semester) must complete the core requirement in at most 4 long semesters after the core completion begins. Failure to complete the core requirements with the required grades and GPA within the core completion period normally results in an M.S. student not being allowed to continue into the Ph.D. program and a Ph.D. student being removed from the Ph.D. program.

Students who began the Ph.D. program Fall 2017 through Summer 2020 : A student must complete a minimum of 66 credit hours subject to the following restrictions:

• At least 30 credit hours of approved, regular, or special topics (COSC xx97) courses. At most 6 of these 30 hours may be outside the Department with the approval of the student’s research advisor and the Director of Graduate Studies.

Students who began the Ph.D. program prior to Fall 2017 : A student must complete a minimum of 72 credit hours subject to the following restrictions:

• At least 36 credits of approved, regular, or special topics (xx97) courses. At most 6 of these hours may be outside the Department with the approval of the student’s research advisor and the Director of Graduate Studies.
• Between 24 and 30 credits of research hours (8x98) to fulfill the 72 hour degree requirement.
• At least 6 but not more than 12 credits of dissertation (8x99).

COSC Academic Policies

The following are required of all COSC PhD students regardless of when they started the program except when noted.

• Declare a research advisor (also known as dissertation committee chair)
• Satisfactory completion of COSC 6110 or COSC 6321
• *Satisfactory completion of the Research Competency Evaluation (RCE) Exam ( *applicable to those in Catalog 2021-2022 and onward ) 
• Satisfactory completion of the Core/Breadth Requirement
• Declare a dissertation committee
• Proposal Defense (preliminary examination): written proposal and satisfactory defense thereof.
• Dissertation Defense: written dissertation and satisfactory defense thereof.
• Satisfactory performance on Annual Reviews
• Publication of doctoral research. It is recommended to work towards one or more publications before the proposal defense and additional publications or submissions before the dissertation defense.
• Attend at least 5 department seminars per term.  Effective January 2018. In addition, all Ph.D. students, regardless of year, are expected to attend at least one (1) Computer Science Focus on Research presentation per academic year. Ph.D. students in the third year or after are expected to present their research during at least one (1) Computer Science Focus on Research presentation per academic year.
• Maintain satisfactory progress. Failure to meet degree, department, college, and university requirements and policies may be dismissed from the PhD program.

Dissertations Committee

The dissertation committee must be comprised of a minimum of four members to include three internal members (inclusive of the research advisor who serves as the dissertation committee chair or co-chair) who have their primary faculty appointment within the major department and one approved external member from outside the major department at UH, industry or other academic institution who is acceptable to the department and approved by the college. A faculty member with a joint appointment in the major department is considered as an external member unless he/she chairs the committee. In this case, an additional external member outside the major department is required. After these minimum requirements for committee members are satisfied, additional committee members may be approved, but at least 50% of the committee must be tenured/tenure-track faculty at the University of Houston. Research faculty, instructional faculty and emeritus faculty may serve on dissertation committees, but not chair the committees. However, a research professor may serve as a co-advisor with a tenured/tenure-track faculty. For the purpose of the committee composition, an emeritus faculty is considered as internal non-tenure-track faculty member.

Proposal Defense

A student must pass a proposal defense (also referred as the preliminary examination) administered by the student’s dissertation committee. The purpose of the proposal defense is to evaluate and give feedback on the proposed dissertation research of the student. Candidates should have one or more publications before the proposal defense and additional publications or submissions before the dissertation defense.

The student must prepare the dissertation proposal document using the NSM PhD dissertation template and present the proposal to the dissertation committee. The proposal document should include an overview of the proposed work, relevant related work, completed work, and a plan for the work to be completed in the dissertation. The presentation should cover the same topics in the proposal document, and include a listing of coursework completed, publications, and a proposed timeline for key activities to complete in the dissertation. The proposal document should be submitted to the dissertation committee at least two weeks before the proposal defense.

Proposal Defense Announcement

The proposal defense is open to the public and should be announced two weeks in advance. All may ask the student questions related to the proposal or the student’s preparation for doctoral-level research. The committee may have a closed session with the student at the end of the proposal defense. Details on how to announce the defense can be found on the Defense Guidelines page.

Proposal Defense Evaluation Outcome

The committee will submit the  Proposal Defense Evaluation Form to the Director of Graduate Studies summarizing the student’s performance of the proposal defense and assign an overall evaluation of satisfactory (pass) or unsatisfactory (fail). A student will be informed of the outcome and upon receiving a satisfactory evaluation the student becomes a Ph.D. candidate.

Proposal Defense Deadline

Full-time and part-time students must attempt the proposal defense no later than the end of the third long term after completing the core/breadth requirement. The proposal defense cannot be held before fulfilling the core/breadth requirement. The proposal defense must be completed at least one term before the dissertation defense.

Dissertation Defense

A PhD candidate will be required to present their dissertation in a public defense. The dissertation defense and the proposal defense may not be scheduled in the same term. The dissertation committee decides the acceptability of the dissertation. Candidates are expected to publish results of their dissertation research prior to the dissertation defense. Candidates should have one or more publications before the proposal defense and additional publications or submissions before the dissertation defense.

Dissertation Defense Announcement

The dissertation defense is open to the University community and the student must inform the department at least two weeks in advance so that it can be publicized. Details on how to announce the defense can be found on the  Defense Guidelines  page.

Annual Review

Every PhD student must complete the formation of a dissertation committee no later than the end of the 2 nd  year in the program. Each student will be reviewed annually by the dissertation committee during a review meeting; the review is mandatory starting on the 3rd year. The review meeting should be integrated to the proposal defense in the year in which the proposal defense takes place, and it is not necessary in the year of the dissertation defense.

After meeting with the student, the dissertation committee will submit a “PhD Annual Review - Committee Evaluation Form” to the Director of Graduate Studies. The evaluation can be satisfactory “S”, unsatisfactory “U”, or needs improvement “NI”. If the student receives a “U” or “NI” grade, the student must be provided with a clear plan to return to a satisfactory status and reviewed again in the next long term. A subsequent evaluation of “NI” or “U” can result in removal of the program.

PhD Annual Review Deadlines

• Self-Evaluation Form must be submitted by the student before the following deadlines: October 31 (during fall reviews) or May 31 (during spring reviews)
• Committee Evaluation Form must be submitted by the dissertation committee no later than the end of the Spring or Fall semester.

Time Limitations

Students who enroll as doctoral candidates must complete their degree requirements within 10 years of the date of first enrollment with a doctoral degree objective. All courses used towards the degree, including transferred and substituted courses, must not be older than 10 years at the time of graduation. Failure to comply will result in the candidate being ineligible for a doctoral degree.

Doctoral students who fail to complete their dissertation within five years after completion of the comprehensive examination must retake the examination.

Refer to the  Time Limitations of Completion of Degree Requirements  section of the Graduate Catalog.

UH and NSM Academic Policies

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The PhD is the primary research degree that can be taken in the Department of Computer Science and Technology. The Cambridge PhD is a three to four-year full-time (five to seven-year part-time) programme of individual research on a topic agreed by the student and the Department, under the guidance of a staff member as the student's supervisor. 

All research students are admitted to read for the PhD degree on a probationary basis and will be registered for the Certificate of Postgraduate Study (CPGS) in the first instance. During this year students may do some additional coursework and will write a research report that is likely to form the foundation of the eventual PhD thesis. The CPGS will involve four components:

Research skills training

Practical work

Research report of no more than 10,000 words

Attendance at a research workshops and research seminars

At the end of the third term and on completion of the CPGS, students whose performance indicates that they would be able to complete a PhD in a reasonable time will be upgraded to PhD status. A student who is not upgraded to PhD status, and who has completed three terms of study, will normally be awarded the CPGS alone. They will not submit a thesis for the PhD degree.

There is an expectation that all research students will contribute to teaching in the department for which some training will be provided. Research students will submit a log - or tally-sheet - of teaching activities annually at the end of June. 

Students are expected to complete the substance of their research by the end of their third year, submitting their thesis then or within a few months.

Applicants wishing to apply to undertake a PhD on a part-time basis should refer to the Department's admissions advice for potential part-time students.

The Postgraduate Virtual Open Day usually takes place at the end of October. It’s a great opportunity to ask questions to admissions staff and academics, explore the Colleges virtually, and to find out more about courses, the application process and funding opportunities. Visit the  Postgraduate Open Day  page for more details.

See further the  Postgraduate Admissions Events  pages for other events relating to Postgraduate study, including study fairs, visits and international events.

Key Information

3-4 years full-time, 4-7 years part-time, study mode : research, doctor of philosophy, department of computer science and technology, course - related enquiries, application - related enquiries, course on department website, dates and deadlines:, lent 2024 (closed).

Some courses can close early. See the Deadlines page for guidance on when to apply.

Michaelmas 2024 (Closed)

Funding deadlines.

These deadlines apply to applications for courses starting in Michaelmas 2024, Lent 2025 and Easter 2025.

Similar Courses

• Advanced Computer Science MPhil
• Machine Learning and Machine Intelligence MPhil
• Research in Agri-Food Robotics EPSRC CDT PhD
• Biological Science (Babraham Institute) PhD
• Biological Sciences (Developmental Biology) by advanced study MPhil

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Ph.D. Topics in Computer Science

While there are many topics, you should choose the research topic according to your personal interest. However, the topic should also be chosen on market demand. The topic must address the common people’s problems.

In this blog post, we are listing important and popular Ph.D. (Research) topics in Computer Science .

PhD in Computer Science 2023: Admission, Eligibility

Page Contents

The hottest topics in computer science

• Artificial Intelligence.
• Machine Learning Algorithms.
• Deep Learning.
• Computer Vision.
• Natural Language Processing.
• Blockchain.
• Various applications of ML range: Healthcare, Urban Transportation, Smart Environments, Social Networks, etc.
• Autonomous systems.
• Data Privacy and Security.
• Lightweight and Battery efficient Communication Protocols.
• Sensor Networks
• 5G and its protocols.
• Quantum Computing.
• Cryptography.

Cybersecurity

• Bioinformatics/Biotechnology
• Computer Vision/Image Processing
• Cloud Computing

Other good research topics for Ph.D. in computer science

Bioinformatics.

• Modeling Biological systems.
• Analysis of protein expressions.
• computational evolutionary biology.
• Genome annotation.
• sequence Analysis.

Internet of things

• adaptive systems and model at runtime.
• machine-to-machine communications and IoT.
• Routing and control protocols.
• 5G Network and internet of things.
• Body sensors networks, smart portable devices.

Cloud computing

• How to negotiate service level platform.
• backup options for the cloud.
• Secure data management, within and across data centers.
• Cloud access control and key management.
• secure computation outsourcing.
• most enormous data breach in the 21st century.
• understanding authorization infrastructures.
• cybersecurity while downloading files.
• social engineering and its importance.
• Big data adoption and analytics of a cloud computing platform.
• Identify fake news in real-time.
• neural machine translation to the local language.
• lightweight big data analytics as a service.
• automated deployment of spark clusters.

Machine learning

• The classification technique for face spoof detection in an artificial neural network.
• Neuromorphic computing computer vision.
• online fraud detection.
• the purpose technique for prediction analysis in data mining.
• virtual personal assistant’s predictions.

More posts to read :

• How to start a Ph.D. research program in India?
• Best tools, and websites for Ph.D. students/ researchers/ graduates
• Ph.D. Six-Month Progress Report Sample/ Format
• UGC guidelines for Ph.D. thesis submission 2021

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Digital Commons @ USF > College of Engineering > Computer Science and Engineering > Theses and Dissertations

Computer Science and Engineering Theses and Dissertations

Theses/dissertations from 2023 2023.

Refining the Machine Learning Pipeline for US-based Public Transit Systems , Jennifer Adorno

Insect Classification and Explainability from Image Data via Deep Learning Techniques , Tanvir Hossain Bhuiyan

Brain-Inspired Spatio-Temporal Learning with Application to Robotics , Thiago André Ferreira Medeiros

Evaluating Methods for Improving DNN Robustness Against Adversarial Attacks , Laureano Griffin

Analyzing Multi-Robot Leader-Follower Formations in Obstacle-Laden Environments , Zachary J. Hinnen

Secure Lightweight Cryptographic Hardware Constructions for Deeply Embedded Systems , Jasmin Kaur

A Psychometric Analysis of Natural Language Inference Using Transformer Language Models , Antonio Laverghetta Jr.

Graph Analysis on Social Networks , Shen Lu

Deep Learning-based Automatic Stereology for High- and Low-magnification Images , Hunter Morera

Deciphering Trends and Tactics: Data-driven Techniques for Forecasting Information Spread and Detecting Coordinated Campaigns in Social Media , Kin Wai Ng Lugo

Automated Approaches to Enable Innovative Civic Applications from Citizen Generated Imagery , Hye Seon Yi

Theses/Dissertations from 2022 2022

Towards High Performing and Reliable Deep Convolutional Neural Network Models for Typically Limited Medical Imaging Datasets , Kaoutar Ben Ahmed

Task Progress Assessment and Monitoring Using Self-Supervised Learning , Sainath Reddy Bobbala

Towards More Task-Generalized and Explainable AI Through Psychometrics , Alec Braynen

A Multiple Input Multiple Output Framework for the Automatic Optical Fractionator-based Cell Counting in Z-Stacks Using Deep Learning , Palak Dave

On the Reliability of Wearable Sensors for Assessing Movement Disorder-Related Gait Quality and Imbalance: A Case Study of Multiple Sclerosis , Steven Díaz Hernández

Securing Critical Cyber Infrastructures and Functionalities via Machine Learning Empowered Strategies , Tao Hou

Social Media Time Series Forecasting and User-Level Activity Prediction with Gradient Boosting, Deep Learning, and Data Augmentation , Fred Mubang

A Study of Deep Learning Silhouette Extractors for Gait Recognition , Sneha Oladhri

Analyzing Decision-making in Robot Soccer for Attacking Behaviors , Justin Rodney

Generative Spatio-Temporal and Multimodal Analysis of Neonatal Pain , Md Sirajus Salekin

Secure Hardware Constructions for Fault Detection of Lattice-based Post-quantum Cryptosystems , Ausmita Sarker

Adaptive Multi-scale Place Cell Representations and Replay for Spatial Navigation and Learning in Autonomous Robots , Pablo Scleidorovich

Predicting the Number of Objects in a Robotic Grasp , Utkarsh Tamrakar

Humanoid Robot Motion Control for Ramps and Stairs , Tommy Truong

Preventing Variadic Function Attacks Through Argument Width Counting , Brennan Ward

Theses/Dissertations from 2021 2021

Knowledge Extraction and Inference Based on Visual Understanding of Cooking Contents , Ahmad Babaeian Babaeian Jelodar

Efficient Post-Quantum and Compact Cryptographic Constructions for the Internet of Things , Rouzbeh Behnia

Efficient Hardware Constructions for Error Detection of Post-Quantum Cryptographic Schemes , Alvaro Cintas Canto

Using Hyper-Dimensional Spanning Trees to Improve Structure Preservation During Dimensionality Reduction , Curtis Thomas Davis

Design, Deployment, and Validation of Computer Vision Techniques for Societal Scale Applications , Arup Kanti Dey

AffectiveTDA: Using Topological Data Analysis to Improve Analysis and Explainability in Affective Computing , Hamza Elhamdadi

Automatic Detection of Vehicles in Satellite Images for Economic Monitoring , Cole Hill

Analysis of Contextual Emotions Using Multimodal Data , Saurabh Hinduja

Data-driven Studies on Social Networks: Privacy and Simulation , Yasanka Sameera Horawalavithana

Automated Identification of Stages in Gonotrophic Cycle of Mosquitoes Using Computer Vision Techniques , Sherzod Kariev

Exploring the Use of Neural Transformers for Psycholinguistics , Antonio Laverghetta Jr.

Secure VLSI Hardware Design Against Intellectual Property (IP) Theft and Cryptographic Vulnerabilities , Matthew Dean Lewandowski

Turkic Interlingua: A Case Study of Machine Translation in Low-resource Languages , Jamshidbek Mirzakhalov

Automated Wound Segmentation and Dimension Measurement Using RGB-D Image , Chih-Yun Pai

Constructing Frameworks for Task-Optimized Visualizations , Ghulam Jilani Abdul Rahim Quadri

Trilateration-Based Localization in Known Environments with Object Detection , Valeria M. Salas Pacheco

Recognizing Patterns from Vital Signs Using Spectrograms , Sidharth Srivatsav Sribhashyam

Recognizing Emotion in the Wild Using Multimodal Data , Shivam Srivastava

A Modular Framework for Multi-Rotor Unmanned Aerial Vehicles for Military Operations , Dante Tezza

Human-centered Cybersecurity Research — Anthropological Findings from Two Longitudinal Studies , Anwesh Tuladhar

Learning State-Dependent Sensor Measurement Models To Improve Robot Localization Accuracy , Troi André Williams

Human-centric Cybersecurity Research: From Trapping the Bad Guys to Helping the Good Ones , Armin Ziaie Tabari

Theses/Dissertations from 2020 2020

Classifying Emotions with EEG and Peripheral Physiological Data Using 1D Convolutional Long Short-Term Memory Neural Network , Rupal Agarwal

Keyless Anti-Jamming Communication via Randomized DSSS , Ahmad Alagil

Active Deep Learning Method to Automate Unbiased Stereology Cell Counting , Saeed Alahmari

Composition of Atomic-Obligation Security Policies , Yan Cao Albright

Action Recognition Using the Motion Taxonomy , Maxat Alibayev

Sentiment Analysis in Peer Review , Zachariah J. Beasley

Spatial Heterogeneity Utilization in CT Images for Lung Nodule Classication , Dmitrii Cherezov

Feature Selection Via Random Subsets Of Uncorrelated Features , Long Kim Dang

Unifying Security Policy Enforcement: Theory and Practice , Shamaria Engram

PsiDB: A Framework for Batched Query Processing and Optimization , Mehrad Eslami

Composition of Atomic-Obligation Security Policies , Danielle Ferguson

Algorithms To Profile Driver Behavior From Zero-permission Embedded Sensors , Bharti Goel

The Efficiency and Accuracy of YOLO for Neonate Face Detection in the Clinical Setting , Jacqueline Hausmann

Beyond the Hype: Challenges of Neural Networks as Applied to Social Networks , Anthony Hernandez

Privacy-Preserving and Functional Information Systems , Thang Hoang

Managing Off-Grid Power Use for Solar Fueled Residences with Smart Appliances, Prices-to-Devices and IoT , Donnelle L. January

Novel Bit-Sliced In-Memory Computing Based VLSI Architecture for Fast Sobel Edge Detection in IoT Edge Devices , Rajeev Joshi

Edge Computing for Deep Learning-Based Distributed Real-time Object Detection on IoT Constrained Platforms at Low Frame Rate , Lakshmikavya Kalyanam

Establishing Topological Data Analysis: A Comparison of Visualization Techniques , Tanmay J. Kotha

Machine Learning for the Internet of Things: Applications, Implementation, and Security , Vishalini Laguduva Ramnath

System Support of Concurrent Database Query Processing on a GPU , Hao Li

Deep Learning Predictive Modeling with Data Challenges (Small, Big, or Imbalanced) , Renhao Liu

Countermeasures Against Various Network Attacks Using Machine Learning Methods , Yi Li

Towards Safe Power Oversubscription and Energy Efficiency of Data Centers , Sulav Malla

Design of Support Measures for Counting Frequent Patterns in Graphs , Jinghan Meng

Automating the Classification of Mosquito Specimens Using Image Processing Techniques , Mona Minakshi

Models of Secure Software Enforcement and Development , Hernan M. Palombo

Functional Object-Oriented Network: A Knowledge Representation for Service Robotics , David Andrés Paulius Ramos

Lung Nodule Malignancy Prediction from Computed Tomography Images Using Deep Learning , Rahul Paul

Algorithms and Framework for Computing 2-body Statistics on Graphics Processing Units , Napath Pitaksirianan

Efficient Viewshed Computation Algorithms On GPUs and CPUs , Faisal F. Qarah

Relational Joins on GPUs for In-Memory Database Query Processing , Ran Rui

Micro-architectural Countermeasures for Control Flow and Misspeculation Based Software Attacks , Love Kumar Sah

Efficient Forward-Secure and Compact Signatures for the Internet of Things (IoT) , Efe Ulas Akay Seyitoglu

Detecting Symptoms of Chronic Obstructive Pulmonary Disease and Congestive Heart Failure via Cough and Wheezing Sounds Using Smart-Phones and Machine Learning , Anthony Windmon

Toward Culturally Relevant Emotion Detection Using Physiological Signals , Khadija Zanna

Theses/Dissertations from 2019 2019

Beyond Labels and Captions: Contextualizing Grounded Semantics for Explainable Visual Interpretation , Sathyanarayanan Narasimhan Aakur

Empirical Analysis of a Cybersecurity Scoring System , Jaleel Ahmed

Phenomena of Social Dynamics in Online Games , Essa Alhazmi

A Machine Learning Approach to Predicting Community Engagement on Social Media During Disasters , Adel Alshehri

Interactive Fitness Domains in Competitive Coevolutionary Algorithm , ATM Golam Bari

Measuring Influence Across Social Media Platforms: Empirical Analysis Using Symbolic Transfer Entropy , Abhishek Bhattacharjee

A Communication-Centric Framework for Post-Silicon System-on-chip Integration Debug , Yuting Cao

Authentication and SQL-Injection Prevention Techniques in Web Applications , Cagri Cetin

Multimodal Emotion Recognition Using 3D Facial Landmarks, Action Units, and Physiological Data , Diego Fabiano

Robotic Motion Generation by Using Spatial-Temporal Patterns from Human Demonstrations , Yongqiang Huang

A GPU-Based Framework for Parallel Spatial Indexing and Query Processing , Zhila Nouri Lewis

A Flexible, Natural Deduction, Automated Reasoner for Quick Deployment of Non-Classical Logic , Trisha Mukhopadhyay

An Efficient Run-time CFI Check for Embedded Processors to Detect and Prevent Control Flow Based Attacks , Srivarsha Polnati

Force Feedback and Intelligent Workspace Selection for Legged Locomotion Over Uneven Terrain , John Rippetoe

Detecting Digitally Forged Faces in Online Videos , Neilesh Sambhu

Malicious Manipulation in Service-Oriented Network, Software, and Mobile Systems: Threats and Defenses , Dakun Shen

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Computer Science & Engineering

CSE 190 - TOPICS IN COMPUTER SCIENCE AND ENGINEERING (2024-2025)

Updated: May 20th, 2024

CSE 190 - Topics in Computer Science and Engineering

CSE 190 is a topics of special interest in Computer Science and Engineering course. Topics may vary from quarter to quarter.

Prerequisites also vary per course/per instructor. Department approval required.

CSE 190 is typically offered every quarter as staffing allows.

CSE 190 may be repeated for credit a maximum of 3 times (maximum of 12 units; assuming courses taken for a different topic)

A maximum of one  CSE 190 may enrolled/waitlisted per quarter

Note: For the Fall 2023 Computer Science (CS26) curriculum, all CSE 190 courses will be labeled with a corresponding "Tag(s)" (Systems, Theory/Abstraction, and/or Applications of Computing). CSE 190 offerings before Fall 2023 are untagged but may be used as an Open CSE Elective for Computer Science majors who have changed to the FA23 CS26 curriculum.

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CSE 190 A00:  Human-Centered AI with Kristen Vaccaro

Prerequisite:  No course prerequisites. 

CS Curriculum Tag(s): TBD

To enroll:  Submit course clearance request via  Enrollment Authorization System (EASy)

Description:  This course provides an introduction to harnessing the power of AI so that it benefits people and communities. Topics will include: agency and initiative, fairness and bias, transparency and explainability, confidence and errors, and privacy, ethics, and trust. Students will build a number of interactive technologies powered by AI, gain practical experience with what makes them more or less usable, and learn to evaluate their impact on individuals and communities. Students will learn to think critically (but also optimistically) about what AI systems can do and how they should be integrated into society.

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CSE 190 B00:  Programmers are People Too with Michael Coblenz

Prerequisite:  CSE 110 or CSE 130.  Class is unofficially co-scheduled with CSE 291 Section B00. Students will not receive credit for both classes. 

Description:  Programmers of all kinds express their ideas using programming languages. Unfortunately, languages can be hard to use correctly, resulting in lengthy development times and buggy software. How can these languages be designed to make programmers as effective as possible?

In this course, we will learn research methods for analyzing and improving the usability of programming languages. Students will apply these techniques to languages of current and historical interest, and in the process, expand their knowledge of different ways to design languages. This course is intended as preparation for conducting independent research on the usability of programming languages. The first part of the course will emphasize research methods from human-computer interaction research, with examples drawn from programming languages and development environments. The second part of the course will focus on reading research papers that describe key results from the field. The course will include homework assignments as well as a group research project.

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CS6301 - Special Topics in Computer Science

CS 6301 Special Topics in Computer Science (3 semester credit hours) May be repeated for credit as topics vary. Prerequisite: CS 5343 . (3-0) S

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News Roundup Spring 2024

CEGE Spring Graduation Celebration and Order of the Engineer

Forty-seven graduates of the undergraduate and grad student programs (pictured above) in the Department of Civil, Environmental, and Geo- Engineering took part in the Order of the Engineer on graduation day. Distinguished Speakers at this departmental event included Katrina Kessler (MS EnvE 2021), Commissioner of the Minnesota Pollution Control Agency, and student Brian Balquist. Following this event, students participated in the college-wide Commencement Ceremony at 3M Arena at Mariucci. 

UNIVERSITY & DEPARTMENT

The University of Minnesota’s Crookston, Duluth, and Rochester campuses have been awarded the Carnegie Elective Classification for Community Engagement, joining the Twin Cities (2006, 2015) and Morris campuses (2015), and making the U of M the country’s first and only university system at which every individual campus has received this selective designation. Only 368 from nearly 4,000 qualifying U.S. universities and colleges have been granted this designation.

CEGE contributed strongly to the College of Science and Engineering’s efforts toward sustainability research. CEGE researchers are bringing in over $35 million in funded research to study carbon mineralization, nature and urban areas, circularity of water resources, and global snowfall patterns. This news was highlighted in the Fall 2023 issue of  Inventing Tomorrow  (pages 10-11). https://issuu.com/inventingtomorrow/docs/fall_2023_inventing_tomorrow-web

CEGE’s new program for a one-year master’s degree in structural engineering is now accepting applicants for Fall 2024. We owe a big thanks to DAN MURPHY and LAURA AMUNDSON for their volunteer work to help curate the program with Professor JIA-LIANG LE and EBRAHIM SHEMSHADIAN, the program director. Potential students and companies interested in hosting a summer intern can contact Ebrahim Shemshadian ( [email protected] ).

BERNIE BULLERT , CEGE benefactor and MN Water Research Fund founder, was profiled on the website of the University of Minnesota Foundation (UMF). There you can read more about his mission to share clean water technologies with smaller communities in Minnesota. Many have joined Bullert in this mission. MWRF Recognizes their Generous 2024 Partners. Gold Partners: Bernie Bullert, Hawkins, Inc., Minnesota Department of Health, Minnesota Pollution Control Agency, and SL-serco. Silver Partners: ISG, Karl and Pam Streed, Kasco, Kelly Lange-Haider and Mark Haider, ME Simpson, Naeem Qureshi, Dr. Paul H. Boening, TKDA, and Waterous. Bronze Partners: Bruce R. Bullert; Brenda Lenz, Ph.D., APRN FNP-C, CNE; CDM Smith; Central States Water Environment Association (CSWEA MN); Heidi and Steve Hamilton; Jim “Bulldog” Sadler; Lisa and Del Cerney; Magney Construction; Sambatek; Shannon and John Wolkerstorfer; Stantec; and Tenon Systems.

After retiring from Baker-Tilly,  NICK DRAGISICH  (BCE 1977) has taken on a new role: City Council member in Lake Elmo, Minnesota. After earning his BCE from the University of Minnesota, Dragisich earned a master’s degree in business administration from the University of St. Thomas. Dragisich retired in May from his position as managing director at Baker Tilly, where he had previously served as firm director. Prior to that, he served as assistant city manager in Spokane, Washington, was the city administrator and city engineer in Virginia, Minnesota, and was mayor of Chisholm, Minnesota—all adding up to more than 40 years of experience in local government. Dragisich was selected by a unanimous vote. His current term expires in December 2024.

PAUL F. GNIRK  (Ph.D. 1966) passed away January 29, 2024, at the age of 86. A memorial service was held Saturday, February 24, at the South Dakota School of Mines and Technology (SDSM&T), where he started and ended his teaching career, though he had many other positions, professional and voluntary. In 2018 Paul was inducted into the SDSM&T Hardrocker Hall of Fame, and in 2022, he was inducted into the South Dakota Hall of Fame, joining his mother Adeline S. Gnirk, who had been inducted in 1987 for her work authoring nine books on the history of south central South Dakota.

ROGER M. HILL  (BCE 1957) passed away on January 13, 2024, at the age of 90. His daughter, Kelly Robinson, wrote to CEGE that Roger was “a dedicated Gopher fan until the end, and we enjoyed many football games together in recent years. Thank you for everything.”

KAUSER JAHAN  (Ph.D. 1993, advised by Walter Maier), PE, is now a civil and environmental engineering professor and department head at Henry M. Rowan College of Engineering. Jahan was awarded a 3-year (2022- 2025), $500,000 grant from the U.S. Department of Environmental Protection Agency (USEPA). The grant supports her project, “WaterWorks: Developing the New Generation of Workforce for Water/Wastewater Utilities,” for the development of educational tools that will expose and prepare today’s students for careers in water and wastewater utilities.

SAURA JOST  (BCE 2010, advised by Timothy LaPara) was elected to the St. Paul City Council for Ward 3. She is part of the historic group of women that make up the nation’s first all-female city council in a large city.

The 2024 ASCE Western Great Lakes Student Symposium combines several competitions for students involved in ASCE. CEGE sent a large contingent of competitors to Chicago. Each of the competition groups won awards: Ethics Paper 1st place Hans Lagerquist; Sustainable Solutions team 1st place overall in (qualifying them for the National competition in Utah in June); GeoWall 2nd place overall; Men’s Sprint for Concrete Canoe with rowers Sakthi Sundaram Saravanan and Owen McDonald 2nd place; Product Prototype for Concrete Canoe 2nd place; Steel Bridge (200 lb bridge weight) 2nd place in lightness; Scavenger Hunt 3rd place; and Aesthetics and Structural Efficiency for Steel Bridge 4th place.

Students competing on the Minnesota Environmental Engineers, Scientists, and Enthusiasts (MEESE) team earned second place in the Conference on the Environment undergraduate student design competition in November 2023. Erin Surdo is the MEESE Faculty Adviser. Pictured are NIKO DESHPANDE, ANNA RETTLER, and SYDNEY OLSON.

The CEGE CLASS OF 2023 raised money to help reduce the financial barrier for fellow students taking the Fundamentals of Engineering exam, a cost of $175 per test taker. As a result of this gift, they were able to make the exam more affordable for 15 current CEGE seniors. CEGE students who take the FE exam pass the first time at a rate well above national averages, demonstrating that CEGE does a great job of teaching engineering fundamentals. In 2023, 46 of 50 students passed the challenging exam on the first try.

This winter break, four CEGE students joined 10 other students from the College of Science and Engineering for the global seminar, Design for Life: Water in Tanzania. The students visited numerous sites in Tanzania, collected water source samples, designed rural water systems, and went on safari. Read the trip blog: http://globalblogs.cse.umn.edu/search/label/Tanzania%202024

Undergraduate Honor Student  MALIK KHADAR  (advised by Dr. Paul Capel) received honorable mention for the Computing Research Association (CRA) Outstanding Undergraduate Research Award for undergraduate students who show outstanding research potential in an area of computing research.

GRADUATE STUDENTS

AKASH BHAT  (advised by William Arnold) presented his Ph.D. defense on Friday, October 27, 2023. Bhat’s thesis is “Photolysis of fluorochemicals: Tracking fluorine, use of UV-LEDs, and computational insights.” Bhat’s work investigating the degradation of fluorinated compounds will assist in the future design of fluorinated chemicals such that persistent and/or toxic byproducts are not formed in the environment.

ETHAN BOTMEN  (advised by Bill Arnold) completed his Master of Science Final Exam February 28, 2024. His research topic was Degradation of Fluorinated Compounds by Nucleophilic Attack of Organo-fluorine Functional Groups.

XIATING CHEN , Ph.D. Candidate in Water Resources Engineering at the Saint Anthony Falls Laboratory is the recipient of the 2023 Nels Nelson Memorial Fellowship Award. Chen (advised by Xue Feng) is researching eco-hydrological functions of urban trees and other green infrastructure at both the local and watershed scale, through combined field observations and modeling approaches.

ALICE PRATES BISSO DAMBROZ  has been a Visiting Student Researcher at the University of Minnesota since last August, on a Doctoral Dissertation Research Award from Fulbright. Her CEGE advisor is Dr. Paul Capel. Dambroz is a fourth year Ph.D. student in Soil Science at Universidade Federal de Santa Maria in Brazil, where she studies with her adviser Jean Minella. Her research focuses on the hydrological monitoring of a small agricultural watershed in Southern Brazil, which is located on a transition area between volcanic and sedimentary rocks. Its topography, shallow soils, and land use make it prone to runoff and erosion processes.

Yielding to people in crosswalks should be a very pedestrian topic. Yet graduate student researchers  TIANYI LI, JOSHUA KLAVINS, TE XU, NIAZ MAHMUD ZAFRI  (Dept.of Urban and Regional Planning at Bangladesh University of Engineering and Technology), and Professor Raphael Stern found that drivers often do not yield to pedestrians, but they are influenced by the markings around a crosswalk. Their work was picked up by the  Minnesota Reformer.

TIANYI LI  (Ph.D. student advised by Raphael Stern) also won the Dwight David Eisenhower Transportation (DDET) Fellowship for the third time! Li (center) and Stern (right) are pictured at the Federal Highway Administration with Latoya Jones, the program manager for the DDET Fellowship.

The Three Minute Thesis Contest and the Minnesota Nice trophy has become an annual tradition in CEGE. 2023’s winner was  EHSANUR RAHMAN , a Ph.D. student advised by Boya Xiong.

GUANJU (WILLIAM) WEI , a Ph.D. student advised by Judy Yang, is the recipient of the 2023 Heinz G. Stefan Fellowship. He presented his research entitled Microfluidic Investigation of the Biofilm Growth under Dynamic Fluid Environments and received his award at the St. Anthony Falls Research Laboratory April 9. The results of Wei's research can be used in industrial, medical, and scientific fields to control biofilm growth.

BILL ARNOLD  stars in an award-winning video about prairie potholes. The Prairie Potholes Project film was made with the University of Delaware and highlights Arnold’s NSF research. The official winners of the 2024 Environmental Communications Awards Competition Grand Prize are Jon Cox and Ben Hemmings who produced and directed the film. Graduate student Marcia Pacheco (CFANS/LAAS) and Bill Arnold are the on-screen stars.

Four faculty from CEGE join the Center for Transportation Studies Faculty and Research Scholars for FY24–25:  SEONGJIN CHOI, KETSON ROBERTO MAXIMIANO DOS SANTOS, PEDRAM MORTAZAVI,  and  BENJAMIN WORSFOLD . CTS Scholars are drawn from diverse fields including engineering, planning, computer science, environmental studies, and public policy.

XUE FENG  is coauthor on an article in  Nature Reviews Earth and Environment . The authors evaluate global plant responses to changing rainfall regimes that are now characterized by fewer and larger rainfall events. A news release written at Univ. of Maryland can be found here: https://webhost.essic. umd.edu/april-showers-bring-mayflowers- but-with-drizzles-or-downpours/ A long-running series of U of M research projects aimed at improving stormwater quality are beginning to see practical application by stormwater specialists from the Twin Cities metro area and beyond. JOHN GULLIVER has been studying best practices for stormwater management for about 16 years. Lately, he has focused specifically on mitigating phosphorous contamination. His research was highlighted by the Center for Transportation Studies.

JIAQI LI, BILL ARNOLD,  and  RAYMOND HOZALSKI  published a paper on N-nitrosodimethylamine (NDMA) precursors in Minnesota rivers. “Animal Feedlots and Domestic Wastewater Discharges are Likely Sources of N-Nitrosodimethylamine (NDMA) Precursors in Midwestern Watersheds,” Environmental Science and Technology (January 2024) doi: 10.1021/acs. est.3c09251

ALIREZA KHANI  contributed to MnDOT research on Optimizing Charging Infrastructure for Electric Trucks. Electric options for medium- and heavy-duty electric trucks (e-trucks) are still largely in development. These trucks account for a substantial percentage of transportation greenhouse gas emissions. They have greater power needs and different charging needs than personal EVs. Proactively planning for e-truck charging stations will support MnDOT in helping to achieve the state’s greenhouse gas reduction goals. This research was featured in the webinar “Electrification of the Freight System in Minnesota,” hosted by the University of Minnesota’s Center for Transportation Studies. A recording of the event is now available online.

MICHAEL LEVIN  has developed a unique course for CEGE students on Air Transportation Systems. It is the only class at UMN studying air transportation systems from an infrastructure design and management perspective. Spring 2024 saw the third offering of this course, which is offered for juniors, seniors, and graduate students.

Research Professor  SOFIA (SONIA) MOGILEVSKAYA  has been developing international connections. She visited the University of Seville, Spain, November 13–26, 2023, where she taught a short course titled “Fundamentals of Homogenization in Composites.” She also met with the graduate students to discuss collaborative research with Prof. Vladislav Mantic, from the Group of Continuum Mechanics and Structural Analysis at the University of Seville. Her visit was a part of planned activities within the DIAGONAL Consortium funded by the European Commission. CEGE UMN is a partner organization within DIAGONAL, represented by CEGE professors Mogilevskaya and Joseph Labuz. Mantic will visit CEGE summer 2024 to follow up on research developments and discuss plans for future collaboration and organization of short-term exchange visits for the graduate students from each institution. 

DAVID NEWCOMB  passed away in March. He was a professor in CEGE from 1989–99 in the area of pavement engineering. Newcomb led the research program on asphalt materials characterization. He was the technical director of Mn/ROAD pavement research facility, and he started an enduring collaboration with MnDOT that continues today. In 2000, he moved from Minnesota to become vice-president for Research and Technology at the National Asphalt Pavement Association. Later he moved to his native Texas, where he was appointed to the division head of Materials and Pavement at the Texas A&M Transportation Institute, a position from which he recently retired. He will be greatly missed.

PAIGE NOVAK  won Minnesota ASCE’s 2023 Distinguished Engineer of the Year Award for her contributions to society through her engineering achievements and professional experiences.

The National Science Foundation (NSF) announced ten inaugural (NSF) Regional Innovation Engines awards, with a potential $1.6 billion investment nationally over the next decade. Great Lakes ReNEW is led by the Chicago-based water innovation hub,  Current,  and includes a team from the University of Minnesota, including PAIGE NOVAK. Current will receive $15 mil for the first two years, and up to $160 million over ten years to develop and grow a water-focused innovation engine in the Great Lakes region. The project’s ambitious plan is to create a decarbonized circular “blue economy” to leverage the region’s extraordinary water resources to transform the upper Midwest—Illinois, Indiana, Michigan, Minnesota, Ohio, and Wisconsin. Brewing one pint of beer generates seven pints of wastewater, on average. So what can you do with that wastewater?  PAIGE NOVAK  and her team are exploring the possibilities of capturing pollutants in wastewater and using bacteria to transform them into energy.

BOYA XIONG  has been selected as a recipient of the 2024 40 Under 40 Recognition Program by the American Academy of Environmental Engineers and Scientists. The award was presented at the 2024 AAEES Awards Ceremony, April 11, 2024, at the historic Howard University in Washington, D.C. 

JUDY Q. YANG  received a McKnight Land-Grant Professorship Award. This two-year award recognizes promising assistant professors and is intended to advance the careers of individuals who have the potential to make significant contributions to their departments and their scholarly fields. 

Professor Emeritus CHARLES FAIRHURST , his son CHARLES EDWARD FAIRHURST , and his daughter MARGARET FAIRHURST DURENBERGER were on campus recently to present Department Head Paige Novak with a check for $25,000 for the Charles Fairhurst Fellowship in Earth Resources Engineering in support of graduate students studying geomechanics. The life of Charles Fairhurst through a discussion with his children is featured on the Engineering and Technology History Wiki at https://ethw.org/Oral-History:Charles_Fairhurst#00:00:14_INTRODUCTION

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