research interests example computer science

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How to Contact Faculty for IW/Thesis Advising

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

Parastoo Abtahi, Room 419

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

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

Ryan Adams, Room 411

Research areas:

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

Andrew Appel, Room 209

Available for Fall 2024 IW advising, only

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

Sanjeev Arora, Room 407

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

David August, Room 221

Not available for IW or thesis advising, 2024-2025

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

Mark Braverman, 194 Nassau St., Room 231

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

Sebastian Caldas, 221 Nassau Street, Room 105

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

Bernard Chazelle, 194 Nassau St., Room 301

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

Danqi Chen, Room 412

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

Marcel Dall'Agnol, Corwin 034

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

Jia Deng, Room 423

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

Adji Bousso Dieng, Room 406

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

Robert Dondero, Corwin Hall, Room 038

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

Zeev Dvir, 194 Nassau St., Room 250

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

Benjamin Eysenbach, Room 416

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

Christiane Fellbaum, 1-S-14 Green

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

Adam Finkelstein, Room 424 

• Research Areas: computer graphics, audio.

Robert S. Fish, Corwin Hall, Room 037

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

Michael Freedman, Room 308 

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

Ruth Fong, Room 032

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

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

Tom Griffiths, Room 405

Available for Fall 2024 single-semester IW advising, only

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

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

Aarti Gupta, Room 220

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

Elad Hazan, Room 409  

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

Felix Heide, Room 410

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

Peter Henderson , 302 Sherrerd Hall

• Research Areas: Machine learning, law, and policy

Kyle Jamieson, Room 306

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

Alan Kaplan, 221 Nassau Street, Room 105

Research Areas:

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

Brian Kernighan, Room 311

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

Zachary Kincaid, Room 219

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

Gillat Kol, Room 316

• Research area: theory

Aleksandra Korolova, 309 Sherrerd Hall

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

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

Pravesh Kothari, Room 320

• Research areas: Theory

Amit Levy, Room 307

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

Kai Li, Room 321

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

Xiaoyan Li, 221 Nassau Street, Room 104

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

Lydia Liu, Room 414

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

Wyatt Lloyd, Room 323

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

Alex Lombardi , Room 312

• Research Areas: Theory

Margaret Martonosi, Room 208

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

Jonathan Mayer, Sherrerd Hall, Room 307 

Available for Spring 2025 single-semester IW, only

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

Mae Milano, Room 307

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

Andrés Monroy-Hernández, Room 405

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

Christopher Moretti, Corwin Hall, Room 036

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

Radhika Nagpal, F316 Engineering Quadrangle

• Research areas: control, robotics and dynamical systems

Karthik Narasimhan, Room 422

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

Arvind Narayanan, 308 Sherrerd Hall 

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

Pedro Paredes, Corwin Hall, Room 041

My primary research work is in Theoretical Computer Science.

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

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

 1. Theoretical research

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

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

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

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

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

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

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

Iasonas Petras, Corwin Hall, Room 033

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

1.   Quantum algorithms and circuits:

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

2.   Information Based Complexity:

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

3. Topics in Scientific Computation:

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

Yuri Pritykin, 245 Carl Icahn Lab

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

Benjamin Raphael, Room 309  

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

Vikram Ramaswamy, 035 Corwin Hall

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

Ran Raz, Room 240

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

Szymon Rusinkiewicz, Room 406

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

Olga Russakovsky, Room 408

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

Sebastian Seung, Princeton Neuroscience Institute, Room 153

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

Jaswinder Pal Singh, Room 324

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

Mona Singh, Room 420

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

Robert Tarjan, 194 Nassau St., Room 308

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

Olga Troyanskaya, Room 320

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

David Walker, Room 211

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

Shengyi Wang, Postdoctoral Research Associate, Room 216

Available for Fall 2024 single-semester IW, only

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

Kevin Wayne, Corwin Hall, Room 040

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

Matt Weinberg, 194 Nassau St., Room 222

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

Huacheng Yu, Room 310

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

Ellen Zhong, Room 314

Opportunities outside the department.

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

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

Maria Apostolaki, Engineering Quadrangle, C330

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

Branko Glisic, Engineering Quadrangle, Room E330

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

Mihir Kshirsagar, Sherrerd Hall, Room 315

Center for Information Technology Policy.

• Consumer protection
• Content regulation
• Competition law
• Economic development
• Surveillance and discrimination

Sharad Malik, Engineering Quadrangle, Room B224

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

• Design of reliable hardware systems
• Verifying complex software and hardware systems

Prateek Mittal, Engineering Quadrangle, Room B236

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

Ken Norman,  Psychology Dept, PNI 137

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

Potential research topics

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

Caroline Savage

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

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

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

Other potential projects include:

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

Janet Vertesi, Sociology Dept, Wallace Hall, Room 122

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

David Wentzlaff, Engineering Quadrangle, Room 228

Computing, Operating Systems, Sustainable Computing.

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

• Graduate School

Research Interest Statement Samples That Worked

A good research interest statement sample can be hard to find. Still, it can also be a beneficial tool for writing one and preparing for a grad school application or post-graduate position. Your research interest statement is one of the key components of your application to get into grad school . In a few cases, admissions committees have used it instead of an interview, so it is important to write a strong essay. We’ve provided research interest statement samples for you in this blog post. We have also included several tips that will help you write a strong statement to help improve your chances of getting accepted into your dream program. 

>> Want us to help you get accepted? Schedule a free strategy call here . <<

Article Contents 13 min read

What is a research interest statement.

A research interest statement is essential for most graduate school, post-graduate, and academic job applications. Sometimes, it may be referred to it as a " statement of intent " or "description of research interests." While they are similar, research interest statement may require some additional information. Generally, your statement will pride a brief overview of your research background, including your past research experience, the current state of your research, and the future research you'd like to complete, including any required equipment and collaborations. It is usually written in the form of a short essay. Still, of course, different graduate programs can have specific requirements, so make sure to check the program you are applying to and read the particular instructions that they give to ensure your research interest statement meets their requirements. 

Your research statement plays a big role in the committee's decision. Ultimately, they are trying to figure out if you, as a person, and your research, would be a good fit for their program. A strong statement can help you convince them of this by showing your passion for research, your research interests and experience, the connection between your interests and the program, and the extent of your writing skills which is really important for paper and grant writing, and thus for earning money for your research!

Undergraduate programs are centered around classes, but graduate and post-graduate programs are all about your research and what your research contributes to your discipline of choice. That is why a research interest statement is so important, because it is essentially a way for you to share this information with the program that you have chosen.

Writing a strong statement can be helpful to you, as well. Having to explain your research and talk about your goals coherently will give you a chance to define your future research and career plans, as well as academic interests.

What Should Your Research Interest Statement Include?

The exact requirements of the research interest statement can vary depending on where you are applying and for what position. Most faculty positions will need you to produce a separate file for your statement, and most of the time, for an academic program, you can simply include your statement within your CV for graduate school .  

Need to prepare your grad school CV? This video has helpful advice for you:

Unless otherwise stated by the program or faculty that you are applying to, your statement should be one to two pages long or between 600 and 1000 words. If you are including your description of interest statements on your resume, then it would be ideal to keep it between 400 and 600 words. Most programs will give you guidelines for the research interest statement so make sure you follow those. They rarely include a specific question or prompt but they might ask for a particular detail to be included in your interest statement. For example, a university’s requirements may look something like this: “In your statement of interest, you should detail your study and/or research interests and reasons for seeking admission. You must identify a faculty member from the Anthropology of Department with whom you are interested in being your advisor. The length of a statement of intent should be 2 pages in length (single-spaced, Times New Roman font size 12 point)”

Your statement should include a brief history of your past research. It should tell the committee what you have previously set out to answer with your research projects, what you found, and if it led to any academic publications or collaborations. It should also address your current research. What questions are you actively trying to solve? You will need to tell the committee if you’ve made any progress, what you have found, if you are connecting your research to the larger academic conversation and what the larger implications of your work actually are. Finally, you want to talk about the future of your research. What further questions do you want to solve? How do you intend to find answers to these questions? What are the broader implications of your potential results, and how can the institution you are applying to help you?

Before we show you some examples, let's go over a few essential things that you need to keep in mind while writing your research interest statement to make sure it is strong. 

Preparation

Give Yourself Ample Time: Much like with other components of your application, like your CV or a graduate school interview question , preparation is the key to success. You should give yourself enough time to thoroughly research the program or faculty you are applying to, gather all the information or documents that can aid you in writing, and then write and rewrite as many times as you need to. Give yourself at least 6 weeks to draft, redraft, and finalize your statement. You may also want to consider investing in a graduate school admissions consultant as they have more experience writing these types of essays and may see things that you can’t.

Research the Program/Faculty: The purpose of your research interest statement is to tell the committee all about your research plans, how it will contribute to the field and convince them that not only is their institution is the best place for it, but that you will be an asset to them as a candidate. To do this, you need to know what kind of candidate they are looking for, what kind of research they have been interested in in the past, and if there is anything particular that they require in the research interest statement. Remember, expectations for research statements can vary among disciplines and universities, so it is essential that you write for the right audience.

The Format / Writing Style

Your research statement should be in an academic essay format. It needs to be concise, well-organized, and easy to read. For graduate school, PhD or post-doc positions, your research interest statement will usually be a part of your resume. We recommend that you stick to the following things when it comes to the format:

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The Content

Introduction: This is a functional academic document, unlike college essays or personal statements, so you want to go straight to the point and focus on the key information that needs to be conveyed. You want to use this paragraph to tell the committee why you are writing this statement. In other words, you should clearly state what kind of research you are interested in pursuing at the institution in question and explain why you are drawn to the subject. 

Body: This is your “why and how” paragraphs. In 2 or 3 paragraphs, you should expand on your interest, background, accomplishments, and plans in the field of research. Depending on your level of experience, you may use this time to talk about your previous or current research. If you do not have much experience, then you may use this paragraph to talk about any skills or academic achievements that could be relevant. 

Conclusion: To conclude, you should restate your interest and tie it back to the research you intend to continue at the university. Be specific about the direction you’d like to take the research in, who you’d like to work with, and what the institution has that would help you. We also suggest including a concise statement that reiterates your unique suitability for the program, and what you can contribute to it and your chosen field.

Common Pitfalls to Avoid

Being Too Personal: Often, students will confuse the statement of purpose and the research interest statement or letter of intent. It is essential to understand the difference between these two documents because some programs will ask for both of these documents. There is quite a bit of overlap between the two essays, so they are very easy to mix up. Both documents ask applicants to focus on their research interests, relevant past academic & professional experiences, and their long-term goals in the field. However, a statement of purpose is more of a personal statement that describes your journey and overall suitability for a program. In contrast, a research interest statement is a more formal academic document specific to the research you intend to pursue in a program. It will include many details such as the faculty members you want to work with, the program facilities and resources you wish to use, etc.

Not Following Guidelines: As mentioned earlier, these statements can vary depending on the discipline and the faculty. It is crucial that you review all the institution's guidelines and follow them. Some schools will have a specific word count, others may simply give you a maximum and minimum word count. Others may even have a specific prompt or question that you will need to answer with your essay. You want to make sure that you are following the instructions provided by the program. 

Using Too Much Jargon: Your statement will be read by people who are most likely knowledgeable, but they might not be from your specific field or specialty. We understand that it may not be possible to be clear about your research without using a few niche words, but try to keep them at a minimum and avoid using acronyms that are not well known outside of your specialty.

Having One Generic Statement: The requirements of your research statement are different from one school to another, and you should tailor your letter to the program you are writing to. We know that the research and experience you are talking about are still the same, but the qualities and aspects of that experience you play up should help you appeal to the school you are applying to. For example, if you are applying to a very collaborative program, you should highlight your collaborations and your experience working as part of a team.

Looking for tips on getting into grad school? This infographic is for you:

Research of Interest Statement Samples

Below are sample research interest statements for reference: 

Research Statement of Interest 1

Jennifer Doe

As the child of an immigrant, I have always been fascinated by the relationship between identity, geographic territory, and economic development. With the rise of globalization, there is a broader effort in the social sciences to study the link between cultural identity, human mobility, and economic development in the contemporary world. I hope that my research will contribute to this as well. I am applying to the X University Global Anthropology program, as it is the best place for me to explore my research interests and channel them towards my long-term goals. I believe that my undergraduate education and the research experience it gave me have prepared me to undertake advanced research projects, thus making me an excellent candidate for this program.

I spent the first two years of undergraduate studies taking psychology courses. I went to university knowing that I wanted to learn about human behavior and culture. I was thirsty for information, but I did not know what kind of information just yet. It wasn’t until I took an elective anthropology class in my second year and started discussing identity in anthropology that something clicked. Unlike many other social sciences, anthropology explores the different ways that cultures affect human behavior and that connected right away with my experience as an immigrant. I have been passionate about the subject ever since, and I intend on spending my career exploring this topic further.

In the long run, I am interested in understanding how geography affects the construction of one’s cultural identity, especially when it comes to immigrants. Literature already exists on the topic, but most of it examines the upper levels of this process of social reproduction, concentrating on the roles of governments and associations in promoting ties between migrants and their homelands. Prof. Jane Doe Smith is one of the anthropologists researching the transnational migration experience, and I hope to have the opportunity to work with her at X University.

I was fortunate to be part of a summer research experience as an undergraduate, which took place in several west African countries, including Mali, Senegal, and Nigeria. Dr. Sam Smith was leading the research, and my time on his team allowed me to gain hands-on experience in research while living abroad. One of the things that I did almost daily was interview the subjects in a controlled environment, and sometimes I got to be a part of traditional ceremonies. I learnt how to observe without being intrusive and how to interact with clinical subjects. The experience only strengthened my curiosity and conviction that today more than ever, we need to understand what identity is and the different factors that can affect it.

I enrolled in several challenging research-oriented courses such as Applied Statistical Inference for the Behavioral Sciences, Principles of Measurement, and more throughout my degree. I was also able to work as a research lab assistant for one of my mentors, Mr. Jonathan Smith. I worked with him while he studied the relationship between identity, culture and “self.” My main duties were to assist in the creating of surveys and other assessment materials, administer written and verbal tests to participants, create literature reviews for potential resources, create summaries of findings for analysis and other office duties such as reserving testing rooms. This particular experience allowed me to get some hands-on experience with data collection, data analysis, report preparation and the creation of data summaries.

I know that there is a lot more that I can learn from the X University. I have seen the exemplary work in anthropology and other social studies done by the staff and alumni of this school. It has inspired and convinced me beyond the shadow of a doubt that pursuing my graduate studies in your program meets my personal, academic, and professional goals objectives.

My advanced research skills, passion for anthropology and clinical research, as well as my academic proficiency make me the ideal candidate for X University's Clinical Global Anthropology Master’s program. I believe that X University’s rigorous curriculum and facilities make it the perfect place for me, my long-term career goals and my research commitments. 

Jamie Medicine

I am applying to the brain and development master's program of X university because it is one of the few universities that not only has a program that combines the two disciplines that I majored in my undergraduate studies: Psychology and Linguistics; but also because it is a program that I know would allow me to grow as a researcher, contribute to my chosen fields and achieve my long-term career goals. My research is motivated by two of my favorite things: language and music. To be more specific, hip-hop music. In 20xx, Rollingstone magazine published an article stating that hip hop was now more popular than rock and roll. The rise in popularity of this initially very niche genre has sparked a conversation in specific academic fields such as psychology, sociology, linguistics, and English about the use of language within it but also the effects that it can have on those who listen to it. I hope to one day contribute to that conversation by studying the relationship between hip-hop music and vocabulary development, and I believe that pursuing this particular research interest at X university is the best way for me to do that.

There are many potential places this research may lead me and many potential topics I may explore. Furthermore, there are many things that it would allow us to learn about the effect that music has on our brains and society at large.

I was fortunate enough to work under Dr. Jane D. Smith at the University of X for two years while conducting her recently published study on vocabulary instruction for children with a developmental language disorder. During my time in her lab, I interviewed participants and put together evaluation materials for them. I was also responsible for data entry, analysis, and summarizing. This experience gave me the skills and the knowledge that allowed me to exceed expectations for my final research project in undergraduate school.

One of my undergraduate degree requirements was to complete a small independent study under the supervision of a professor. I chose to study music's effect on children's vocabulary development. Several studies look for ways to decrease the million-word gap, and I wanted to see if this thing that I am so passionate about, music, had any effect at all. I compiled multiple literature reviews and analyzed their results, and I found that there is indeed a correlation between the number of words that a child spoke and the amount of music that they were exposed to. 

This research is currently being explored on a larger scale by Prof. John Doe at X university and learning from him is one of the many reasons I have applied to this program. I took several research methodology courses throughout my degree, and I would love to enroll in the Applied Statistics for Psychology course he is currently teaching to build upon the foundational knowledge I already have. There are several other faculty members in the brain and language department with whom learning from would be a dream come true. In addition to that, working with them is a real possibility because the research they are currently doing and the research I hope to pursue are greatly matched.

I genuinely believe that X university has the curriculum and facilities that I need to meet my long-term goals and research commitments. I also believe that my academic achievements, eagerness to learn, and passion make me the perfect candidate for your program. 

Interested in some tips to help you manage grad school once you're there? Check out this video :

It is essentially an essay that provides a brief overview of your research experience and goals. This includes your past research experience, the current state of your research, and the future research you'd like to complete. It is also sometimes referred to as a "statement of intent" or "description of research interests."

This statement tells the admissions committee more about you as an applicant. It gives you the opportunity to tell them more about your research (past, present, and future) and show them that you are a good fit for their institution.

No. Some graduate school programs might ask for a statement of purpose and a writing sample instead, or they could ask for none of the above. You should always check the requirements of the specific program that you’re applying to.

Generally, your statement should be 400 to 1000 words or about two pages long. That said, most programs will give you guidelines so make sure you check those and follow them.

You certainly can but we do not recommend it. You should always tailor your statement to the program you are applying to. Remember that the aim is to convince the admissions committee that you are a good fit for their school so make sure you highlight the qualities and values that they care about.

We recommend that you doublecheck the information provided by your chosen program as they often have specific instructions for the format of the letter. If none exist, make sure that the format of your document is pleasing to the eye. Stick to easily legible fonts, a decent font size, spacing, margins, etc.  Also, it is best to keep the content of the letter concise and professional.

We recommend giving yourself at least 6 weeks to write your statement. This will give you ample time to brainstorm, write a strong letter, read it again and edit it as many times as necessary. It also gives you enough time to get expert eyes on your letter and work with them to improve it if you wish.

No. Research interest statements are often required for post-graduate school applications and for other positions in academic faculties.

Absolutely! You can always reach out to admissions professionals, such as graduate school admissions consultants or grad school essays tutors .

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Thank you for your excellent site

BeMo Academic Consulting

You are very welcome, Rasool!

Sadia Sultana

hello, thanks for providing guide line for Research Interest statement, the important aspect of scholarship application. Kindly guide me, What should be the title of the Research Statement. Thanks

Hi Sadia! Check the requirements of your school first. They might provide some info on whether a title is even needed. 

Sadia Tasnim Epa

I'm very pleased that you have mentioned every detail of research interest which helped me to clear all of my doubts.... Thank you very much.

Hi Sadia! Glad you found this helpful!

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Research Topics & Ideas: CompSci & IT

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

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

NB – This is just the start…

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

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

Overview: CompSci Research Topics

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

Topics/Ideas: Algorithms & Data Structures

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

Topics & Ideas: Artificial Intelligence (AI)

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

Topics & Ideas: Networking

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

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.

You Might Also Like:

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

Steps on getting this project topic

I want to work with this topic, am requesting materials to guide.

Information Technology -MSc program

It’s really interesting but how can I have access to the materials to guide me through my work?

That’s my problem also.

Investigating the impacts of software refactoring techniques and tools in blockchain-based developments is in my favour. May i get the proper material about that ?

BLOCKCHAIN TECHNOLOGY

I NEED TOPIC

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• Ultrasound Modeling of Human organs (heart, liver)
• Bio-signatures of in-vivo cells
• Thomas Wischgoll
• Advanced Visual Data Analysis (AViDA)

Cloud computing is a major step toward organizing all aspects of computation as a public utility service. It embraces concepts such as software as a service and platform as a service, including services for workflow facilities, application design and development, deployment and hosting services, data integration, and management of software. The cloud platform increases in importance as our industry makes the phase change from in-house data management to cloud-hosted data management to improve efficiency and focus on core businesses. However, like any new technology, there are formidable problems, from performance issues to security and privacy, from metadata management to massively parallel execution.

This is a major part of the Kno.e.sis Research Center.

• Cloud infrastructure for data management
• Privacy and security in cloud data management
• Cloud-based mining and learning algorithms
• Cloud support for text mining and web search
• Large-scale natural language modeling and translation
• Parallel and distributed algorithms for bioinformatics
• Performance evaluation and benchmarking
• Database Research Laboratory
• Bioinformatics Research Group

The Department of Computer Science and Engineering of Wright State University recently received a grant, titled "REU Site: Cybersecurity Research at Wright State University", from the National Science Foundation. This NSF REU site offers a ten-week summer program that aims at providing a diverse group of motivated undergraduates with competitive research experiences in cyber-security research. A variety of projects will be offered in Network Security, Intrusion Detection, Wireless Sensor Network Security, Internet Malware Detection, Analysis, and Mitigation, Software Reverse Engineering and Vulnerability Discovery, and Privacy-Preserving Data Mining. More information of this REU Site can be found at http://reu.cs.wright.edu .  

In addition there are two ongoing projects sponsored by DARPA and ONR for Deepfake techniques, Deep Understanding of Technical Documents, and Computer Security (like memory attacks).

• Junjie Zhang
• WSU Cybersecurity Lab

Related Programs

• Master of Science in Cybersecurity
• Undergraduate

Cyber-Physical Systems are jointly physical and computational and are characterized by complex loops of cause and effect between the computational and physical components. We focus on the creation of methods by which such systems can self-adapt to repair damage and exploit opportunities and methods by which we can explain and understand how they operate even after having diverged from their original forms. Our current application area the creation of control systems for insect-like flapping-wing air vehicles that repair themselves, in flight, after suffering wing damage.

Click here for more information about Cyber Physical Systems at Wright State University

Data mining is the process of extracting useful knowledge from a database. Data mining facilitates the characterization, classification, clustering, and searching of different databases, including text data, image and video data, and bioinformatics data for various applications. Text, multimedia, and bioinformatics databases are very large and so parallel/distributed data mining is essential for scalable performance.

• Parallel/distributed data mining
• Text/image clustering and categorization
• Metadata for timelining events
• XML database
• Data warehousing
• Biological/medical data mining
• Data Mining Research Lab

Data Science and Analytics

Mathematical, statistical, and graphical methods for exploring large and complex data sets.  Methods include statistical pattern recognition, multivariate data analysis, classifiers, modeling and simulation, and scientific visualization.

• Topological Data Analysis
• Predictive Analytics
• Michelle Cheatham
• Machine Learning and Complex Systems Lab
• Data Science for Healthcare

Multimedia systems offer synergistic and integrated solutions to a great variety of applications related to multi-modality data, such as automatic target recognition, surveillance, tracking human behavior, etc.

• Object recognition in digital images and video
• Multimedia content classification and indexing
• Integrated search and retrieval in multimedia repositories
• Background elimination in live video
• Modeling and visualization
• Biometrics and cyber security
• Network and security visualization

Semantic, Social and Sensor Webs

The World Wide Web contains rapidly growing amount of enterprise, social, device/sensor/IoT/WoT data in unstructured, semistructured and structured forms. The Semantic Web initiative by the World Wide Web consortium (W3C) of which Wright State University is a member (represented by Kno.e.sis) has developed standards and technologies to associate meaning to data, to make data more machine and human understandable, and to apply reasoning techniques for intelligent processing leading to actionable information, insights, and discovery. Kno.e.sis has one of the largest academic groups in the US in Semantic Web, and its applications for better use and analysis of social and sensor data.

• Computer assisted document interpretation tools
• Information extraction from semi-structured documents
• Semantic Web knowledge representation
• Semantic sensor web
• Linked and Big Data

Machine Learning and Artificial Intelligence

Machine learning and artificial intelligence aim to develop computer systems that exhibit intelligent behavior in decision making, object recognition, planning, learning, and other applications that require intelligent assessment of complex information.  Our faculty apply modern tools such as deep neural networks, evolutionary algorithms, statistical inference, topological analysis, and graphical inference models to a wide variety of problems from engineering, science, and medicine.

• Knowledge Representation and Reasoning
• Intelligent agents
• Natural language understanding
• Evolutionary algorithms and evolvable hardware
• Autonomous robotic systems
• Machine learning
• Fuzzy and neural systems
• Intelligent control systems
• Deep Neural Networks

Wireless communication and networking have revolutionized the way people communicate. Currently, there are more than two billion cellular telephone subscribers worldwide. Wireless local area networks have become a necessity in many parts of the globe. With new wireless enabled applications being proposed every day, such as wireless sensor networks, telemedicine, music telepresence, and intelligent web, the potential of this discipline is just being unleashed.

• Ultra-high speed optical network
• Wireless sensor network
• Music telepresence
• Cognitive radio and dynamic spectrum access
• Secure protocol and secure processors authentication
• Cyber-physical systems
• Network coding

Take the Next Step

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Research Areas

Autonomous and cyber-physical systems.

Subareas: Real-time and Embedded Systems, Sensor Systems, Mobile Computing, Control Theory and Systems, Formal Methods, Automated Verification and Certification Faculty:  Alterovitz ,  Anderson , Chakraborty , Duggirala ,  Nirjon

More on Autonomous and Cyber-Physical Systems

Bioinformatics and computational biology.

Subareas: Computational Genetics, Computational Immunology, Proteomics, Statistical Genetics, Single-Cell Bioinformatics Faculty: Ahalt ,  Krishnamurthy , Marron , McMillan , Snoeyink , Stanley

More on Bioinformatics and Computational Biology

Computational Immunology: Advancements in high-throughput flow and mass cytometry technologies have enabled the ability to study the immune system at an unparalleled depth.  Understanding immunological adaptations to particular diseases and in aging and development offers unique opportunities to develop novel diagnostic tests or to propose specialized treatments or lifestyle interventions to optimize human health. Using single-cell flow and mass cytometry data collected across multiple individuals, our goal is to develop new computational techniques to identify and link heterogeneity in the cellular landscape to external variables of interest, such as, a clinical phenotype or diagnosis. Recent advances in imaging cytometry also enable taking images of tissues and studying the spatial organization of immune cells. Application areas of interest include pregnancy, HIV, neuroimmunology, and T-cell biology. Relevant People Natalie Stanley ; Collaborating Departments: Microbiology and Immunology , Computational Medicine Program , Department of Anesthesia

Development and Differentiation, and Metagenomics: We use novel measurement techniques as well as machine learning methods in understanding the interplay between these areas, with the aim of discovering the forces that shape the immune system throughout life. The overarching goal is to apply the insights from such analyses to propose new treatments for cancers.

Single-Cell Bioinformatics: Cellular heterogeneity, or the synergy of diverse and specialized cell-types drive a range of biological phenomena. Several technologies exist for measuring various properties (e.g. gene expression, protein expression) in individual cells, which allows for their comprehensive characterization and analysis in clinical or biological applications. Single-cell measurements can be studied in vitro to understand the etiology of disease.  For example, in hypoxia of heart muscle cells, the  cells become scar tissue and lose their muscle function.  This process can be studied by looking at single cell transcriptomes to determine the order of events. Further, it can be possible to “reprogram” this sequence of events to avoid the adverse outcome. See some of our recent work in reprogramming scar tissue cells to recover some heat muscle cell functionality.

Technologies and Data Science Problems:   Single-cell datasets produced with technologies, such as single-cell RNA sequencing (scRNA-seq) or flow and mass cytometry reveal a unique data structure where there are several high-dimensional single-cell measurements per profiled sample, which need to be efficiently integrated. 

Flow and Mass Cytometry : Flow and mass cytometry are high-throughput single-cell proteomics technologies for systematic analysis of the immune system. Often applied for the analysis of human blood and tissue samples, the produced datasets can collectively contain millions of cells. We focus on developing new computational techniques for representing, dissecting, and mining this large volume of cells to identify immunological adaptations in disease and development. 

Relevant People: Leonard McMillan , Natalie Stanley

Computer Architecture

Subareas: Accelerators, Clockless Logic, Energy-efficient Computing, Security Faculty: Porter , Singh , Sturton

More on Computer Architecture

Energy-Efficient Systems: With the explosive growth in mobile devices, there has been a push towards increasing energy efficiency of computation for longer battery life. Reducing power consumption is also important for desktop computing to alleviate challenges of heat removal and power delivery. A special focus in our department has been on the development of energy-efficient graphics hardware. Another area of future interest is energy-harvesting systems, which are ultra-low-power systems that operate on energy scavenged from the environment.

Asynchronous or Clockless Computing: Asynchronous VLSI design is poised to play a key role in the design of the next generation of microelectronic chips. By dispensing with global clocks and instead using flexible handshaking between components, asynchronous design offers the benefits of lower power consumption, greater ease of integration of multiple cores, and greater robustness to manufacturing and runtime variation. Our researchers work on all aspects of asynchronous design, including circuits, architectures, and CAD tools. A key area of interest is application to network-on-a-chip for integration of multiple heterogeneous cores.

Computer Graphics

Subareas: Animation & Simulation, Graphics Hardware, Modeling, Rendering, Tracking, Virtual Environments, Visualization Faculty: Alterovitz , Chakravarthula , Fuchs , Marks , Sengupta , Singh , Snoeyink , Daniel Szafir , Danielle Szafir

More on Computer Graphics

Computer vision.

Subareas: Geometric Vision, Language & Vision, Recognition Faculty: Ahalt , Bansal , Bertasius , Marks , Niethammer , Sengupta

More on Computer Vision

Human-computer interaction.

Subareas: Assistive Technology, Haptics, Human Factors Analysis, Sound & Audio Display, User-Interface Toolkits, Virtual Environments Faculty: Dewan , Marks , Nirjon , Porter , Pozefsky , Srivastava , Stotts , Daniel Szafir , Danielle Szafir

More on Human-Computer Interaction

Wearable devices, such as smart watches and smart glasses, and other common sensors are increasingly facilitating new modes of interaction with modern computers—making the goal of ubiquitous computing realizable. A major research direction in HCI at UNC is exploring design techniques and system support to more easily extend desktop and phone applications onto devices with widely varying form factors and interaction modes.

Another significant research direction at UNC is exploring assistive technologies for users with impairments, such as learning disabilities, blindness, and low vision. These populations face significant barriers to education and employment that we aim to reduce, as well as study different modes of interaction with computers.

Machine Learning and Data Science

Subareas: Data Integration, Internet of Things, Knowledge Discovery, Machine Learning, Scientific Data Management, Visual Analytics Faculty: Ahalt , Bansal , Bertasius , Chaturvedi , Krishnamurthy , Marks , McMillan , Niethammer , Nirjon , Oliva , Sengupta , Srivastava , Danielle Szafir , Yao

More on Machine Learning and Data Science

Machine Learning: The problems we study combine vast amounts and disparate types of measurements with equally complex prior knowledge, posing unique challenges for machine learning. Our interests include both modeling paradigms, such as Bayesian nonparametric methods, and inference methodologies, such as MCMC, variational methods and convex optimization.  We also work on structured, interpretable, and generalizable deep learning models. Other topics of focus include multi-task learning, reinforcement learning, and transfer learning.

Medical Image Analysis

Subareas: Biomechanical Modeling, Diffusion Imaging, Image-guided Interventions, Segmentation, Shape Analysis, Registration Faculty: Alterovitz , Marron , Niethammer , Oguz , Pizer , Styner

More on Medical Image Analysis

Natural language processing.

Subareas: Language Generation, Multimodal and Grounded NLP (with Vision and Robotics), Question Answering and Dialogue Faculty:  Bansal , Chaturvedi , Srivastava

More on Natural Language Processing

Subareas: Distributed Systems, Internet Measurements, Multimedia Systems, Multimedia Transport, Network Protocols Faculty: Aikat , Dewan , Jeffay , Kaur , Mayer-Patel , Nirjon , Pozefsky

More on Networking

Operating systems.

Subareas: File Systems, Virtualization, Concurrency, Software Support for Secure Hardware Faculty: Anderson , B. Berg , Jeffay , Porter

More on Operating Systems

This area has substantial overlap with a number of other research areas, including cyber-physical systems, real-time systems, mobile systems, networking, architecture, human-computer interaction, and security.

Real-Time Systems

Faculty: Anderson , Jeffay , Nirjon

More on Real-Time Systems

Subareas: Assistive Robotics, Manipulation, Medical Robotics, Motion Planning & Control, Robot Learning, Robot Perception (see: Computer Vision) Faculty: Alterovitz , Bansal , Snoeyink , Daniel Szafir

More on Robotics

Subareas: Cloud Computing Security, Cryptography, Hardware Security, Mobile Device Security, Network Security Faculty: Aikat , Eskandarian , Kwong , Porter , Sturton

More on Security

Network security: Today’s Internet infrastructure is a common target of attack and the vehicle for numerous unwanted activities in network applications (e.g., spam, phishing).  We are conducting research to evaluate the extent of these vulnerabilities and to develop defenses against them. This includes research on both protecting the Internet infrastructure from attack and designing defenses within the context of network applications.

Cloud computing security: The use of cloud servers to outsource data and processing has become increasingly common. Because cloud facilities are shared, however, a customer’s data and processing may reside with those of competitors or attackers, and so privacy and integrity of the customer’s activities are paramount. We are developing technologies to better protect data and processing in such threatening environments.

Subareas: Algorithms, Automated Theorem Proving, Formal Methods Faculty: Anderson , B. Berg , Duggirala , Eskandarian , Snoeyink , Sturton

More on Theory

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Research in Computer Science

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Security and privacy.

A stable, safe, and resilient cyberspace is vital for our economic and societal wellbeing. This concentration helps students learn how to fortify cyber networks, combat threats, and foster “white hat” hacking. Researching systems allows for students to improve real-world systems to make them stronger and securer. This also includes data-driven analysis of privacy and social networks. After graduation, our students often work either in private corporations or in governments.

Labs:  OSIRIS ,  C CS

Sample research projects:

Damon McCoy ,  one of the department's newest faculty members, researched counterfeit pharmacy affiliate networks. Online sales of counterfeit or unauthorized products drive a robust underground advertising industry that includes email spam, “black hat” search engine optimization, forum abuse and so on. Virtually everyone has encountered enticements to purchase drugs, prescription-free, from an online “Canadian Pharmacy.” However, even though such sites are clearly economically motivated, the shape of the underlying business enterprise is not well understood precisely because it is “underground.”

Learn more about the business of online pharmaceutical affiliate programs

Learn more about Digital Assembly

Learn more about Seattle Open Peer-to-Peer Computing

Big Data Management, Analysis, and Visualization

The organization and governance of large volumes of data. This concentration allows for retaining data obtained from a large number of sources — from a large city, to individuals, and anywhere in between — and ensures a high level of data quality for analytical purposes. The visualization of such data elegantly brings structure and simplicity to it.

Labs:   CUSP

Learn more about RevEx and download the demo

In this related paper, Gerig studies the early developing brain by displaying the longitudinal MRI scans of the same subject's brain at various ages, from two weeks to two years.

Learn more about  Prof. Gerig's study

In Prof. Chunara's research on US obesity rates, for example, Facebook is used to cross-measure user interests and obesity prevalence within certain metroplitan populations. Activity-related interests across the US and sedentary-related interests across NYC were significantly associated with obesity prevalence.

Learn more about Chunara's study

Prof. Ergan is also the head of  the Future Building Informatics and Visualization Lab (biLab).

Game Engineering and Computational Intelligence

For students who are interested in learning game programming and taking part in game development and design. Computer graphics, human-computer interaction, artificial intelligence, and allied computational fields all play a role in this burgeoning industry. Art and engineering intersect to create innovative game environments that captivate players.

Labs:  Game Innovation Lab ,  MAGNET

Professor  Julian Togelius  specializes in artificial intelligence, and has programmed AI agents that play several existing video games. In the clip above, an AI agent plays through Super Mario Bros.

Learn more about Professor Julian Togelius's project

Algorithms and Foundations

The theoretical study of computer science allows us to better understand the capabilities and the limitations of exactly what problems computers can solve, and when they can solve those problems efficiently. New theory helps pave the way for algorithmic breakthroughs that engineers can build on to create new solutions and technology. At NYU Tandon, the Algorithms and Foundations group is composed of researchers interested in applying mathematical and theoretical tools to a variety of disciplines in computer science, from machine learning, to computational science, to geometry, to computational biology, and beyond.

Christopher Musco  and doctoral student Raphael A. Meyer wrote a paper titled “Hutch++: Optimal Stochastic Trace Estimation” that introduces an new randomized algorithm for implicit trace estimation, a linear algebra problem with applications ranging from computational chemistry, to understanding social networks and deep neural networks. Their method is the first to improve on the popular Hutchinson’s method for the problem, which was introduced over 30 years ago. Read the paper

Lisa Hellerstein  is the co-author of "The Stochastic Score Classification Problem." This paper presents approximation algorithms for evaluating a symmetric Boolean function in a stochastic environment. The algorithms address problems where the goal is to determine the order in which to perform a sequence of tests, so as to minimize expected testing cost. Read the paper

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Computer science articles from across Nature Portfolio

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

Meta’s AI translation model embraces overlooked languages

More than 7,000 languages are in use throughout the world, but popular translation tools cannot deal with most of them. A translation model that was tested on under-represented languages takes a key step towards a solution.

• David I. Adelani

Latest Research and Reviews

Emergent digital bio-computation through spatial diffusion and engineered bacteria

Biological computing is a promising field with potential applications in biosafety, environmental monitoring, and personalized medicine. Here the authors create bio-computers using engineered E. coli colonies that respond to chemical gradients, producing different logic functions depending on how they are spatially arranged.

• Alex J. H. Fedorec
• Neythen J. Treloar
• Chris P. Barnes

Ultra-high-granularity detector simulation with intra-event aware generative adversarial network and self-supervised relational reasoning

Simulating responses of a full particle physics detector with high granularity is computationally very expensive. Here, the authors develop a deep generative model that is able to model a detector with millions of information channel with good performances, reducing both storage demand and CPU time.

• Baran Hashemi
• Nikolai Hartmann
• Thomas Kuhr

Biologically meaningful genome interpretation models to address data underdetermination for the leaf and seed ionome prediction in Arabidopsis thaliana

• Daniele Raimondi
• Antoine Passemiers
• Yves Moreau

Application of density clustering with noise combined with particle swarm optimization in UWB indoor positioning

• Haozhou Yin
• Daokuan Ren

Software cost estimation predication using a convolutional neural network and particle swarm optimization algorithm

• Moatasem. M. Draz
• Safaa. M. Azzam

Basketball technique action recognition using 3D convolutional neural networks

• Jingfei Wang
• Carlos Cordente Martínez

News and Comment

Accelerating AI: the cutting-edge chips powering the computing revolution

Engineers are harnessing the powers of graphics processing units (GPUs) and more, with a bevy of tricks to meet the computational demands of artificial intelligence.

• Dan Garisto

Who owns your voice? Scarlett Johansson OpenAI complaint raises questions

In the age of artificial intelligence, situations are emerging that challenge the laws over rights to a persona.

• Nicola Jones

Anglo-American bias could make generative AI an invisible intellectual cage

• Queenie Luo
• Michael Puett

AlphaFold3 — why did Nature publish it without its code?

Criticism of our decision to publish AlphaFold3 raises important questions. We welcome readers’ views.

Back to basics to open the black box

Most research efforts in machine learning focus on performance and are detached from an explanation of the behaviour of the model. We call for going back to basics of machine learning methods, with more focus on the development of a basic understanding grounded in statistical theory.

• Diego Marcondes
• Adilson Simonis
• Junior Barrera

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25+ Research Ideas in Computer Science for High School Students

As a high school student, you may be wondering how to take your interest in computer science to the next level. One way to do so is by pursuing a research project. By conducting research in computer science, you can deepen your understanding of this field, gain valuable skills, and make a contribution to the broader community. With more colleges going test-optional, a great research project will also help you stand out in an authentic way!

Research experience can help you develop critical thinking, problem-solving, and communication skills. These skills are valuable not only in computer science but also in many other fields. Moreover, research experience can be a valuable asset when applying to college or for scholarships, as it demonstrates your intellectual curiosity and commitment to learning.

Ambitious high school students who are selected for the Lumiere Research Scholar Programs work on a research area of their interest and receive 1-1 mentorship by top Ph.D. scholars. Below, we share some of the research ideas that have been proposed by our research mentors – we hope they inspire you!

Topic 1: Generative AI

Tools such as ChatGPT, Jasper.ai, StableDiffusion and NeuralText have taken the world by storm. But this is just one major application of what AI is capable of accomplishing. These are deep learning-based models , a field of computer science that is inspired by the structure of the human brain and tries to build systems that can learn! AI is a vast field with substantial overlaps with machine learning , with multiple intersections with disciplines such as medicine, art, and other STEM subjects. You could pick any of the following topics (as an example) on which to base your research.

1. Research on how to use AI systems to create tools that augment human skills. For example, how to use AI to create detailed templates for websites, apps, and all sorts of technical and non-technical documentation

2. Research on how to create multi-modal systems. For example, use AI to create a chatbot that can allow users Q&A capabilities on the contents of a podcast series, a television show, and a very diverse range of content.

3. Research on how to use AI to create tools that can do automated checks for quality and ease of understanding for student essays and other natural language tasks. This can help students quickly improve their writing skills by improving the feedback mechanism.

4. Develop a computer vision system to monitor wildlife populations in a specific region.

5. Investigate the use of computer vision in detecting and diagnosing medical conditions from medical images.

6. Extracting fashion trends (or insert any other observable here) from public street scene data (i.e. Google Street View, dash cam datasets, etc.)

Ideas by a Lumiere Mentor from Cornell University.

Topic 2: Data Science

As a budding computer scientist, you must have studied the importance of sound, accurate data that can be used by computer systems for multiple uses. A good example of data science used in education is tools that help calculate your chances of admission to a particular college. By collecting a small amount of data from you, and by comparing it with a much larger database that has been refined and updated regularly, these tools effectively use data science to calculate acceptance rates for students in a matter of seconds.

Another area is Natural Language Processing, or NLP, for short, aims to understand and improve machines' ability to understand and interpret human language. Be it the auto-moderation of content on Reddit, or developing more helpful, intuitive chatbots, you can pick any research idea that you're interested in.

You could pick one of the following, or related questions to study, that come under the umbrella of data science.

7. Develop a predictive model to forecast traffic congestion in your city.

8. Analyze the relationship between social media usage and mental health outcomes in a specific demographic.

9. Investigate the use of data analytics in reducing energy consumption in commercial buildings.

10. Develop a chatbot that can answer questions about a specific topic or domain, such as healthcare or sports.

11. Learn the different machine learning and natural language processing methods to categorize text (e.g. Amazon reviews) as positive or negative.

12. Investigate the use of natural language processing techniques in sentiment analysis of social media data.

Ideas by a Lumiere Mentor from the University of California, Irvine.

Topic 3: Robotics

A perfect research area if you're interested in both engineering and computer science , robotics is a vast field with multiple real-world applications. Robotics as a research area is a lot more hands-on than the other topics covered in this blog, so it's a good idea to make a note of all the possible tools, guides, time, and space that you may need for the following ideas. You can also pitch some of these ideas to your school if equipped with a robotics lab so that you can conduct your research in the safety of your school, and also receive guidance from your teachers!

13. Design and build a robot that can perform a specific task, such as picking up and stacking blocks.

14. Investigate the use of robots in medicine, such as high-precision surgical robots.

15. Develop algorithms to enable a robot to navigate and interact with an unfamiliar environment.

Ideas by a Lumiere Mentor from University College London.

Topic 4: Ethics in computer science

With the rapid development of technology, ethics has become a significant area of study. Ethical principles and moral values in computer science can relate to the design, development, use, and impact of computer systems and technology. It involves analyzing the potential ethical implications of new technologies and considering how they may affect individuals, society, and the environment. Some of the key ethical issues in computer science include privacy, security, fairness, accountability, transparency, and responsibility. If this sounds interesting, you could consider the following topics:

16. Investigate fairness in machine learning. There is growing concern about the potential for machine learning algorithms to perpetuate and amplify biases in data. Research in this area could explore ways to ensure that machine learning models are fair and do not discriminate against certain groups of people.

17. Study the energy consumption and carbon footprint of machine learning can have significant environmental impacts. Research in this area could explore ways to make machine learning more energy-efficient and environmentally sustainable.

18. Conduct Privacy Impact Assessments for a variety of tools for identifying and evaluating the privacy risks associated with a particular technology or system.

Topic 5: Game Development

According to statistics, the number of gamers worldwide is expected to hit 3.32 billion by 2024. This leaves an enormous demand for innovation and research in the field of game design, an exciting field of research. You could explore the field from multiple viewpoints, such as backend game development, analysis of various games, user targeting, as well as using AI to build and improve gaming models. If you're a gamer, or someone interested in game design, pursuing ideas like the one below can be a great starting point for your research -

19. Design and build a serious game that teaches users about a specific topic, such as renewable energy or financial literacy.

20. Analyze the impact of different game mechanics on player engagement and enjoyment.

21. Develop an AI-powered game that can adjust difficulty based on player skill level.

Topic 6: Cybersecurity

According to past research, there are over 2,200 attacks each day which breaks down to nearly 1 cyberattack every 39 seconds. In a world where digital privacy is of utmost importance, research in the field of cybersecurity deals with improving security in online platforms, spotting malware and potential attacks, and protecting databases and systems from malware and cybercrime is an excellent, relevant area of research. Here are a few ideas you could explore -

22. Investigate the use of blockchain technology in enhancing cybersecurity in a specific industry or application.

23. Apply ML to solve real-world security challenges, detect malware, and build solutions to safeguard critical infrastructure.

24. Analyze the effectiveness of different biometric authentication methods in enhancing cybersecurity.

Ideas by Lumiere Mentor from Columbia University

Topic 7: Human-Computer Interaction

Human-Computer Interaction, or HCI, is a growing field in the world of research. As a high school student, tapping into the various applications of HCI-based research can be a fruitful path for further research in college. You can delve into fields such as medicine, marketing, and even design using tools developed using concepts in HCI. Here are a few research ideas that you could pick -

25. Research the use of color in user interfaces and how it affects user experience.

26. Investigate the use of machine learning in predicting and improving user satisfaction with a specific software application.

27. Develop a system to allow individuals with mobility impairments to control computers and mobile devices using eye tracking.

28. Use tools like WAVE or WebAIM to evaluate the accessibility of different websites

Topic 8: Computer Networks

Computer networks refer to the communication channels that allow multiple computers and other devices to connect and communicate with each other. An advantage of conducting research in the field of computer networks is that these networks span from local, regional, and other small-scale networks to global networks. This gives you a great amount of flexibility while scoping out your research, enabling you to study a particular region that is accessible to you and is achievable in terms of time, resources, and complexity. Here are a few ideas -

29. Investigate the use of software-defined networking in enhancing network security and performance.

30. Develop a network traffic classification system to detect and block malicious traffic.

31. Analyze the effectiveness of different network topology designs in reducing network latency and congestion.

Topic 9: Cryptography

Cryptography is the practice of secure communication in the presence of third parties or adversaries. It uses mathematical algorithms and protocols to transform plain text into a form that is unintelligible to unauthorized users - the process known as encryption.

Cryptography has grown in uses - starting from securing communication over the internet, protecting sensitive information like passwords and financial transactions, and securing digital signatures and certificates.

32. Investigating side-channel attacks that exploit weaknesses in the physical implementation of cryptographic systems.

33. Research techniques that can enable secure and private machine learning using cryptographic methods.

Additional topics:

IoT: How can networked devices help us enrich human lives?

Computational Modeling: Using CS to model and study complex systems using math, physics, and computer science. Used for everything from weather forecasts, flight simulators, earthquake prediction, etc.

Parallel and distributed systems: Research into algorithms, operating systems and computer architectures built to operate in a highly parallelized manner and take advantage of large clusters of computing devices to perform highly specialized tasks. Used in data centers, supercomputers and by all major web-scale platforms like Amazon, Google, Facebook, etc.

UI/UX Design: Research into using design to improve all kinds of applications

Social Network Analysis: Exploring social structures through network and graph theory. Was used during COVID to make apps that can alert people about potential vectors of disease – be they places, events or people.

Optimization Techniques: optimization problems are common in all engineering disciplines, as well as AI and Machine Learning. Many of the common algorithms to solve them have been inspired by natural phenomena such as foraging behavior of ants or how birds naturally seem to be able to form large swarms that don’t crash into each other. This is a rich area of research that can help with innumerable problems across the disciplines.

Experimental Design: Research into the design and implementation of experimental procedures. Used in everything from Ai and Machine learning, to medicine, sociology, and most social and natural sciences.

Autonomous vehicle: Research into technical and non-technical aspects (user adoption, driver behavior) of self-driving cars

Augmented and Artificial Reality systems: Research into integrating AR to enhance and enrich everyday human experience. Augmenting gaming or augmented learning, for example.

Customized Hardware Research: Modern applications run on customized hardware. AI systems have their own architecture; crypto, its own. Modern systems have decoders built into your CPU, and this allows for highly compressed high quality video streams to play in real-time. Customized hardware is becoming increasingly critical for next-gen applications, from both a performance and an efficiency lens.

Database Systems: Research in the algorithms, systems, and architecture of database systems to enable effective storage, retrieval and usage of data of different types (text, image, sensor, streaming, etc) and sizes (small to petabytes)

Programming languages: Research into how computing languages translate human thought into machine code, and how the design of the language can significantly modify the kind of tools and applications that can be built in that language.

Bioinformatics and Computational Biology: Research into how computational methods can be applied to biological data such as cell populations, genetic sequences, to make predictions/discovery. Interdisciplinary field involving biology, modeling and simulation, and analytical methods.

If you're looking for a real-world internship that can help boost your resume while applying to college, we recommend Ladder Internships!

Ladder Internships  is a selective program equipping students with virtual internship experiences at startups and nonprofits around the world!  

The startups range across a variety of industries, and each student can select which field they would most love to deep dive into. This is also a great opportunity for students to explore areas they think they might be interested in, and better understand professional career opportunities in those areas.

The startups are based all across the world, with the majority being in the United States, Asia and then Europe and the UK. 

The fields include technology, machine learning and AI, finance, environmental science and sustainability, business and marketing, healthcare and medicine, media and journalism and more.

You can explore all the options here on their application form . As part of their internship, each student will work on a real-world project that is of genuine need to the startup they are working with, and present their work at the end of their internship. In addition to working closely with their manager from the startup, each intern will also work with a Ladder Coach throughout their internship - the Ladder Coach serves as a second mentor and a sounding board, guiding you through the internship and helping you navigate the startup environment. 

Cost : $1490 (Financial Aid Available)

Location:   Remote! You can work from anywhere in the world.

Application deadline:  April 16 and May 14

Program dates:  8 weeks, June to August

Eligibility: Students who can work for 10-20 hours/week, for 8-12 weeks. Open to high school students, undergraduates and gap year students!

Additionally, you can also work on independent research in AI, through Veritas AI's Fellowship Program!

Veritas AI focuses on providing high school students who are passionate about the field of AI a suitable environment to explore their interests. The programs include collaborative learning, project development, and 1-on-1 mentorship.  

These programs are designed and run by Harvard graduate students and alumni and you can expect a great, fulfilling educational experience. Students are expected to have a basic understanding of Python or are recommended to complete the AI scholars program before pursuing the fellowship. 

The   AI Fellowship  program will have students pursue their own independent AI research project. Students work on their own individual research projects over a period of 12-15 weeks and can opt to combine AI with any other field of interest. In the past, students have worked on research papers in the field of AI & medicine, AI & finance, AI & environmental science, AI & education, and more! You can find examples of previous projects   here . 

Location : Virtual

$1,790 for the 10-week AI Scholars program

$4,900 for the 12-15 week AI Fellowship 

$4,700 for both

Need-based financial aid is available. You can apply   here . 

Application deadline : On a rolling basis. Applications for fall cohort have closed September 3, 2023. 

Program dates : Various according to the cohort

Program selectivity : Moderately selective

Eligibility : Ambitious high school students located anywhere in the world. AI Fellowship applicants should either have completed the AI Scholars program or exhibit past experience with AI concepts or Python.

Application Requirements: Online application form, answers to a few questions pertaining to the students background & coding experience, math courses, and areas of interest. 

Additionally, you can check out some summer programs that offer courses in computer science such as the Lumiere Scholars Program !

Stephen is one of the founders of Lumiere and a Harvard College graduate. He founded Lumiere as a PhD student at Harvard Business School. Lumiere is a selective research program where students work 1-1 with a research mentor to develop an independent research paper.

Image source: Stock image

/images/cornell/logo35pt_cornell_white.svg" alt="research interests example computer science"> Cornell University --> Graduate School

Computer science, field description.

The Field of Computer Science is intended for students who are primarily interested in the general aspects of computational processes, both theoretical and practical. Areas of research in the field include architecture, artificial intelligence, computational biology, database systems, graphics, human interaction, machine learning, natural language processing, programming languages, robotics, scientific computing, security, systems and networking, theory of computation, and vision.

Contact Information

110C Gates Hall Cornell University Ithaca, NY  14853

Data and Statistics

• Research Master's Program Statistics
• Doctoral Program Statistics

Field Manual

Subject and degrees.

• Computer Science (M.Eng.) (Cornell Tech (NYC))
• Computer Science (M.Eng.) (Ithaca)
• Computer Science (M.S.) (Ithaca)
• Computer Science (Ph.D.) (Ithaca)
• Robotics (Ph.D.) (Ithaca)

Concentrations by Subject

• artificial intelligence
• computer science
• programming languages and logics
• scientific computing and applications
• theory of computation

Mohamed Abdelfattah

• Campus: Ithaca
• Concentrations: Computer Science: artificial intelligence
• Research Interests: Machine Learning

Jayadev Acharya

• Concentrations: Computer Science: artificial intelligence; theory of computation
• Research Interests: information theory, machine learning, algorithmic statistics

Rachit Agarwal

• Concentrations: Computer Science: systems; theory of computation
• Research Interests: distributed systems, systems for big data analytics, networking, design analysis of algorithms

David H. Albonesi

• Concentrations: Computer Science: computer science; systems
• Research Interests: computer science; operating systems; information organization and retrieval

Lorenzo Alvisi

• Research Interests: theory and practice of dependable distributed computing
• Campus: Cornell Tech (NYC)
• Concentrations: Computer Science: artificial intelligence; computer science
• Research Interests: I work in the intersection of natural language processing, machine learning, vision, and robotics. My current main research focus is algorithms for natural language understanding with specific interest in situated interactions.

Shiri Azenkot

• Concentrations: Computer Science: scientific computing and applications
• Research Interests: human-computer interactions

Kavita Bala

• Concentrations: Computer Science: artificial intelligence; computer science; programming languages and logics; scientific computing and applications
• Research Interests: interactive rendering; global illumination algorithms; image-based modeling and rendering

Siddhartha Banerjee

• Concentrations: Computer Science: theory of computation
• Research Interests: Stochastic Modeling, Design of Scalable Algorithms, Matching Markets and Social Computing, Control of Information-Flows, Learning and Recommendation

Christopher Batten

• Concentrations: Computer Science: systems
• Research Interests: high performance and energy efficient parallel; computer architecture; VSLI design

Tapomayukh Bhattacharjee

• Concentrations: Computer Science: artificial intelligence; scientific computing and applications; systems
• Research Interests: Artificial Intelligence, Human Interaction, Machine Learning, Robotics

David S. Bindel

• Concentrations: Computer Science: computer science; scientific computing and applications
• Research Interests: numerical analysis; numerical linear algebra; modeling microelectromechanical systems; numerical software engineering

Kenneth Paul Birman

• Research Interests: distributed computing; fault-tolerant network systems; distributed systems security; large-scale network applications
• Campus: Ithaca - (Minor Member)
• Research Interests: Architecture and VLSI

Florentina Bunea

• Research Interests: Statistical Machine Learning Theory, Statistical Foundations for Data Science, High Dimensional Statistics

Mark Campbell

• Research Interests: robotics, sensor fusion, machine learning and perception

Claire T Cardie

• Research Interests: natural language processing; machine learning; artificial intelligence

Eshan Chattopadhyay

• Research Interests: use of randomness in computation, computational complexity theory and cryptography

Tanzeem Choudhury

• Research Interests: systems that can learn how humans behave and interact with their environment and each other

Sanjiban Choudhury

• Concentrations: Computer Science: artificial intelligence; scientific computing and applications
• Research Interests: Artificial Intelligence, Machine Learning, Robotics

Michael Ryan Clarkson

• Concentrations: Computer Science: programming languages and logics
• Research Interests: Programming Languages, Security

Alex Conway

• Research Interests: algorithms
• Research Interests: Development of algorithms in applied and computational mathematics (e.g. numerical linear algebra, computational quantum chemistry, and spectral clustering)

Cristian Danescu-Niculescu-Mizil

• Research Interests: Understanding and modeling complex human social behavior using large scale textual data.
• Research Interests: computer graphics; computer vision; human-computer interaction; computational photography

Christopher Matthew De Sa

• Concentrations: Computer Science: artificial intelligence; computer science; systems
• Research Interests: Developing and understanding algorithmic, software, and hardware techniques for high-performance machine learning

Sarah A Dean

• Research Interests: Machine Learning, Robotics

Nicola Lee Dell

• Research Interests: human computer interaction(HCI) and information and communication technologies for development with a focus on designing and evaluating systems that improve the lives of underserved populations in low-income regions

Raaz Dwivedi

• Research Interests: Artificial Intelligence

Shimon Edelman

• Research Interests: vision; computational biology

Ahmed El Alaoui

• Research Interests: High-dimensional statistics and probability, algorithms, statistical physics

Kevin Michael Ellis

• Concentrations: Computer Science: artificial intelligence; programming languages and logics
• Research Interests: Artificial Intelligence, Machine Learning

Deborah Estrin

• Research Interests: mobile systems and applications, participatory sensing, health applications, privacy

Silvia Ferrari

• Research Interests: Distributed systems, computer vision, and robotics

John N. Foster

• Concentrations: Computer Science: computer science; programming languages and logics; systems
• Research Interests: intersection between programming languages, databases and formal methods

Sainyam Galhotra

• Concentrations: Computer Science: artificial intelligence; systems
• Research Interests: Databases, Responsible Data Science, and Causal Inference

Nikhil Garg

• Research Interests: Mechanism/Market design and data science, with a focus on design of democracy, markets, and other societal systems

Ziv Goldfeld

• Research Interests: optimal transport theory, statistical machine learning, information theory, high-dimensional statistic, applied probability and interacting particle systems

Carla P Gomes

• Research Interests: artificial intelligence; computer science

Donald P. Greenberg

• Research Interests: realistic image synthesis; modeling; scientific visualization; computer-aided design; image processing

Giulia Guidi

• Research Interests: Computational Biology, Scientific Computing, systems and Networking

Francois V. Guimbretiere

• Research Interests: systems; computer science
• Research Interests: Systems, Machine Learning

Joseph Yehuda Halpern

• Concentrations: Computer Science: artificial intelligence; computer science; programming languages and logics; theory of computation
• Research Interests: logic; artificial intelligence; distributed computing; reasoning about uncertainty

Bharath Hariharan

• Concentrations: Computer Science: artificial intelligence; computer science; scientific computing and applications
• Research Interests: Rich visual understanding (object recognition, object detection, segmentation and beyond), machine learning (deep learning, convolutional networks)
• Research Interests: machine learning;data mining; info retrieval, & AI, esp. targeting questions that integrally involve both people & computing

Guy Hoffman

• Research Interests: human-robot interaction, human-robot teamwork and collaboration in particular with respect to interaction fluency

Justin Alpine Hsu

• Concentrations: Computer Science: programming languages and logics; theory of computation
• Research Interests: Programming Languages, Theory of Computing

Thorsten Joachims

• Research Interests: machine learning; text-mining; statistical learning theory; information access

Wendy Guang-wen Ju

• Research Interests: Interaction with automation; interaction design research; human robot interaction; automotive interaction design
• Research Interests: human interaction, security
• Research Interests: robotics

Nathan Kallus

• Research Interests: casual inference, machine learning, personalization, optimization in statistics, data-driven optimization under uncertainty, online decision making, decision making and operations in health care

Michael PumShin Kim

• Research Interests: Theory of computation

Robert D. Kleinberg

• Concentrations: Computer Science: artificial intelligence; computer science; theory of computation
• Research Interests: theory of computation and computer science

Jon M Kleinberg

• Concentrations: Computer Science: computer science; theory of computation
• Research Interests: algorithms; combinatorial optimization; computational geometry; computational biology

Allison Koenecke

Dexter Campbell Kozen

• Campus: Ithaca - (Graduate School Professor)
• Concentrations: Computer Science: computer science; programming languages and logics; theory of computation
• Research Interests: theory of computation; proof-carrying code; computational complexity; analysis of algorithms; program logic and semantics

Hadas Kress-Gazit

• Research Interests: robotics; motion planning; task planning; language for robotics; human-robot interaction

Volodymyr Kuleshov

• Research Interests: artificial intelligence, machine learning, and computational genomics

Lillian Jane Lee

• Research Interests: natural language processing

Daniel Dongyuel Lee

• Research Interests: machine learning, robotics, computational neuroscience

Owolabi Legunsen

• Concentrations: Computer Science: programming languages and logics; systems
• Research Interests: software engineering, applied formal methods

Emaad Manzoor

• Research Interests: machine learning

Stephen Robert Marschner

• Research Interests: appearance models for natural materials; 3D scanning; processing scanned geometric data; image-based appearance measurements for 3D objects

Jose Martinez

• Research Interests: multithreaded and multiprocessor architectures for high performance and programmability; microarchitecture; hardware-software interaction

David Matteson

• Research Interests: Mathematical Foundations of Data Science, Data Science in Science, Machine Learning

David Mimno

• Research Interests: Machine learning, text mining, digital humanities

Kristina Monakhova

• Research Interests: Artificial Intelligence, Computational Biology, Machine Learning, Vision

John Gregory Morrisett

• Research Interests: programming languages; compilers; distributed systems; runtime systems

Andrew C Myers

• Research Interests: programming languages
• Research Interests: Social media, data mining, human-computer interaction, computational social science, interactive systems

Rajalakshmi Nandakumar

• Research Interests: wireless networking, Mobile Systems and Mobile Health.

Anil Nerode

• Concentrations: Computer Science: artificial intelligence; computer science; programming languages and logics; systems; theory of computation
• Research Interests: logic; applied mathematics

Tapan Suryakant Parikh

• Research Interests: human-computer interaction; information and communication technologies for development; computer science education

Francesca Parise

• Research Interests: Theory of Computing, Artificial Intelligence

Rafael N. Pass

• Research Interests: theory; computer science

Kirstin Hagelskjaer Petersen

• Research Interests: design and coordination of bio-inspired robot collectives and their natural counterparts

Emma Pierson

Thomas Ristenpart

• Concentrations: Computer Science: computer science; systems; theory of computation
• Research Interests: Software Security, applied and theoretical cryptography

Thijs Jan Roumen

• Research Interests: Digital Fabrication, Human-Computer Interaction

Alexander Matthew Rush

• Research Interests: natural language processing, machine learning

Mert Sabuncu

• Research Interests: computer vision, data mining, machine learning

Adrian Lewis Dequine Sampson

• Research Interests: hardware-software abstractions,including computer architecture, compilers, software engineering

Katya Scheinberg

• Concentrations: Computer Science: scientific computing and applications; theory of computation
• Research Interests: continuous optimization, stochastic optimization, optimization in machine learning, complexity analysis

Fred Barry Schneider

• Research Interests: distributed systems security and fault-tolerance; mobile code; concurrent programming; operating systems
• Research Interests: Theory of Computation

Bart Selman

• Research Interests: artificial intelligence and experimental computer science

Phoebe J Sengers

• Research Interests: culturally embedded computing; human-computer interaction; everyday computing; affective computing; interactive art; autonomous agents

Vitaly Shmatikov

• Research Interests: computer security and privacy

David B Shmoys

• Concentrations: Computer Science: computer science; scientific computing and applications; theory of computation
• Research Interests: scheduling; computational complexity

Alexandra Silva

Rachee Singh

• Research Interests: Security, Systems and networking

Keith N. Snavely

• Research Interests: computer graphics; computer vision

Karthik Sridharan

• Research Interests: Machine Learning, Statistical Learning Theory, Online Learning and Decision Making, Optimization, Empirical Process Theory, Concentration Inequalities, Game Theory

Noah Stephens-Davidowitz

• Research Interests: lattices, cryptography, and theoretical computer science more broadly
• Research Interests: machine learning, reinforcement learning, decision making under uncertainty
• Research Interests: computer networks; large-scale complex networks; stochastic networks and processes; optimization theory; control theory and applications; game theory
• Research Interests: combinatorics; complexity theory; communication networks; QoS and data flow

Angelique Marie Taylor

• Research Interests: Robotics, Artificial Intelligence

Alex John Townsend

• Research Interests: Scientific Computing

Immanuel Trummer

• Research Interests: databases; data science; optimization

Madeleine Richards Udell

• Concentrations: Computer Science: artificial intelligence; scientific computing and applications; theory of computation
• Research Interests: optimization and machine learning for large scale data analysis and control

Robbert Van Renesse

• Research Interests: distributed computing; fault-tolerance; distributed multimedia systems

Marten van Schijndel

• Research Interests: natural language processing, machine learning, cognitive modeling

Anke van Zuylen

• Research Interests: design and analysis of algorithms, combinatorial optimization, polyhedral combinatorics

Aditya Vashistha

• Research Interests: Artificial Intelligence, Human-computer interaction

Aaron B. Wagner

• Research Interests: information theory especially compression, feedback communication, security and quantum information
• Research Interests: data mining, machine learning, health data science

Hakim Weatherspoon

• Research Interests: information systems; distributed systems; network systems; peer-to-peer systems

Kilian Quirin Weinberger

• Research Interests: Machine Learning: high dimensional data analysis, machine learned web-search ranking, sentiment analysis, metric learning, multitask- and transfer-learning settings and bio-medical applications

Stephen B Wicker

• Research Interests: artificial intelligence, concurrency and distributed computing

Mark McMahon Wilde

• Research Interests: Quantum computation

David Paul Williamson

• Research Interests: algorithms; combinatorial optimization; computer science
• Research Interests: Artificial Intelligence and Scientific Computing and Applications
• Research Interests: theory of computation

Christina Lee Yu

• Research Interests: theory of computing, artificial intelligence (machine learning), scientific computing

Ramin Zabih

• Research Interests: computer vision

Cheng Zhang

• Research Interests: ubiquitous computing, wearable computing, human computer interaction

Zhiru Zhang

• Research Interests: computer-aided design methodologies, optimization algorithms, compilers & computer architectures of gigascale integrated systems, esp. systems-on-chips

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Accepted Admissions Blog

Everything you need to know to get Accepted

December 8, 2023

How to Write About Your Research Interests

The most common challenge that my master’s and PhD applicant clients face when writing a statement of research interests or a statement of purpose (SOP) is how to describe in concrete terms what their research interests and goals are. This is understandable. Their ideas are still evolving, and some worry that they’ll later be held to the ideas they stated in their applications, as though they were chiseled in stone. Others simply haven’t yet thought those ideas through very much. 

Take a deep breath! By the time you begin writing your thesis, I promise that no one will pop up and wave your SOP or research interests statement around, saying, “But that’s not what you said here!” Everyone knows that your knowledge and ideas will develop throughout your grad program. 

Here are the two things that a great statement of research interests or SOP will do:

• It  will clearly illustrate to the admissions committee that you possess a depth of interest and comprehension in your field and that you understand what goes into research. You will sound naïve if you talk about ideas that are too vague or nebulous, or ones that cannot be addressed adequately through your discipline.  
• It will explain any relevant background you have in this field, why you find it compelling, and  why you are well suited for this career track . 

Four questions to help you find your statement focus

To narrow your interests into something that is concrete enough for you to be able to write about convincingly, without being overly general, ask yourself these questions:

• What are the broad research questions/issues that interest you? Create a summary of your interests that you can work with, and describe your interests in a sentence – or a paragraph, at most.  
• Within those broad areas of interest, can you begin to focus on more specific questions? If you’re not sure what the current questions/problems are in your field, now is the time to start catching up. Read recent journal publications, and go to conferences if you can. Reading the literature in your field will also give you a sense of how to frame your ideas in the language of your field.  
• Have you done any research in this field already? If so, do you intend to build on your previous work in grad school or go in a new direction?  
• How will your research contribute to the field?

Understanding how to present your goals

Some projects described in SOPs are achievable in the short term, while others are big enough to last a career. If your interests/goals fall into this latter category, acknowledge your ambitions, and try to identify some element of your interests that you can pursue as a first step.

Once you have demonstrated your skills (and past experience) in your field, you will be better equipped to define your next steps. 

Focusing your interests will also involve doing more detailed research about the programs to which you plan to apply. For example, consider the following questions:

• Who might be your research supervisor?  
• How do your interests relate to the work this scholar or these scholars are doing now?  
• How would you contribute to the department and to the discipline?

Your SOP will also address your post-degree, longer-term goals. Consider this: do you envision yourself pursuing a career in research/academia? (For many PhD programs, this remains the department’s formal expectation, even though many PhDs find employment outside the academy.) If you’re applying for a master’s degree, be prepared to discuss what your future plans are and how the degree will help you. 

Working on your SOP or statement of research interests?

Your SOP needs to be direct, informative, and… well… purposeful! When you choose Accepted, we match you with a dedicated advisor who will help you create an SOP that best reflects your experiences, goals, and intense desire to attend your target graduate school program. And did you know that Accepted’s clients have received millions of dollars in scholarship offers? Don’t delay – get started now by checking out our  Graduate School Application Services .

For 25 years, Accepted has helped applicants gain acceptance to top undergraduate and graduate programs. Our expert team of admissions consultants features former admissions directors, PhDs, and professional writers who have advised clients to acceptance at top programs worldwide, including Harvard, Stanford, Yale, Princeton, Penn, Columbia, Oxford, Cambridge, INSEAD, MIT, Caltech, UC Berkeley, and Northwestern. Want an admissions expert to help you get Accepted? Click here to get in touch!

Related Resources:

• STEM Applicants: Why Your Statement of Purpose is So Important
• Three Must-Have Elements of a Good Statement of Purpose
• Writing Your Career Goals Essay

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• SIGCSE Top 10 Paper Awards

Top Ten Computer Science Education Research Papers of the Last 50 Years Recognized

At 50th anniversary sigcse symposium, leading computer science education group highlights research that has shaped the field.

New York, NY, March 2, 2019 – As a capstone to its 50th annual SIGCSE Technical Symposium , leaders of the Association for Computing Machinery (ACM) Special Interest Group on Computer Science Education (SIGCSE) are celebrating the ideas that have shaped the field by recognizing a select group of publications with a “Top Ten Symposium Papers of All Time Award.” The top ten papers were chosen from among the best papers that were presented at the SIGCSE Technical Symposium over the last 49 years.

As part of the Top Ten announcement today in Minneapolis, the coauthors of each top paper will receive a plaque, free conference registration for one co-author to accept the award and up to a total of $2,000 that can be used toward travel for all authors of the top ranked paper.

“In 1969, the year of our first SIGCSE symposium, computing education was a niche specialty” explains SIGCSE Board Chair Amber Settle of DePaul University, of Chicago, USA. “Today, it is an essential skill students need to prepare for the workforce. Computing has become one of the most popular majors in higher education, and more and more students are being introduced to computing in K-12 settings. The Top Ten Symposium Papers of All Time Award will emphasize the outstanding research that underpins and informs how students of all ages learn computing. We also believe that highlighting excellent research will inspire others to enter the computing education field and make their own contributions.”

The Top Ten Symposium Papers are:

1. “ Identifying student misconceptions of programming ” (2010) Lisa C. Kaczmarczyk, Elizabeth R. Petrick, University of California, San Diego; Philip East, University of Northern Iowa; Geoffrey L. Herman, University of Illinois at Urbana-Champaign Computing educators are often baffled by the misconceptions that their CS1 students hold. We need to understand these misconceptions more clearly in order to help students form correct conceptions. This paper describes one stage in the development of a concept inventory for Computing Fundamentals: investigation of student misconceptions in a series of core CS1 topics previously identified as both important and difficult. Formal interviews with students revealed four distinct themes, each containing many interesting misconceptions.

2. “ Improving the CS1 experience with pair programming ” (2003) Nachiappan Nagappan, Laurie Williams, Miriam Ferzli, Eric Wiebe, Kai Yang, Carol Miller, Suzanne Balik, North Carolina State University Pair programming is a practice in which two programmers work collaboratively at one computer, on the same design, algorithm, or code. Prior research indicates that pair programmers produce higher quality code in essentially half the time taken by solo programmers. The authors organized an experiment to assess the efficacy of pair programming in an introductory Computer Science course. Their results indicate that pair programming creates a laboratory environment conducive to more advanced, active learning than traditional labs; students and lab instructors report labs to be more productive and less frustrating.

3. “ Undergraduate women in computer science: experience, motivation and culture ” (1997) Allan Fisher, Jane Margolis, Faye Miller, Carnegie Mellon University During a year-long study, the authors examined the experiences of undergraduate women studying computer science at Carnegie Mellon University, with a specific eye toward understanding the influences and processes whereby they attach themselves to or detach themselves from the field. This report, midway through the two-year project, recaps the goals and methods of the study, reports on their progress and preliminary conclusions, and sketches their plans for the final year and the future beyond this particular project.

4. “ A Multi-institutional Study of Peer Instruction in Introductory Computing ” (2016) Leo Porter, Beth Simon, University of California, San Diego; Dennis Bouvier, Southern Illinois University; Quintin Cutts, University of Glasgow; Scott Grissom, Grand Valley State University; Cynthia Lee, Stanford University; Robert McCartney, University of Connecticut; Daniel Zingaro, University of Toronto Peer Instruction (PI) is a student-centric pedagogy in which students move from the role of passive listeners to active participants in the classroom. This paper adds to this body of knowledge by examining outcomes from seven introductory programming instructors: three novices to PI and four with a range of PI experience. Through common measurements of student perceptions, the authors provide evidence that introductory computing instructors can successfully implement PI in their classrooms.

5. " The introductory programming course in computer science: ten principles " (1978) G. Michael Schneider, University of Minnesota Schneider describes the crucial goals of any introductory programming course while leaving to the reader the design of a specific course to meet these goals. This paper presents ten essential objectives of an initial programming course in Computer Science, regardless of who is teaching or where it is being taught. Schneider attempts to provide an in-depth, philosophical framework for the course called CS1—Computer Programming 1—as described by the ACM Curriculum Committee on Computer Science.

6. “ Constructivism in computer science education ” (1998) Mordechai Ben-Ari, Weizmann Institute of Science Constructivism is a theory of learning which claims that students construct knowledge rather than merely receive and store knowledge transmitted by the teacher. Constructivism has been extremely influential in science and mathematics education, but not in computer science education (CSE). This paper surveys constructivism in the context of CSE, and shows how the theory can supply a theoretical basis for debating issues and evaluating proposals.

7. “ Using software testing to move students from trial-and-error to reflection-in-action ” (2004) Stephen H. Edwards, Virginia Tech Introductory computer science students have relied on a trial and error approach to fixing errors and debugging for too long. Moving to a reflection in action strategy can help students become more successful. Traditional programming assignments are usually assessed in a way that ignores the skills needed for reflection in action, but software testing promotes the hypothesis-forming and experimental validation that are central to this mode of learning. By changing the way assignments are assessed--where students are responsible for demonstrating correctness through testing, and then assessed on how well they achieve this goal--it is possible to reinforce desired skills. Automated feedback can also play a valuable role in encouraging students while also showing them where they can improve.

8. “ What should we teach in an introductory programming course ” (1974) David Gries, Cornell University Gries argues that an introductory course (and its successor) in programming should be concerned with three aspects of programming: 1. How to solve problems, 2. How to describe an algorithmic solution to a problem, and 3. How to verify that an algorithm is correct. In this paper he discusses mainly the first two aspects. He notes that the third is just as important, but if the first two are carried out in a systematic fashion, the third is much easier than commonly supposed.

9. “ Contributing to success in an introductory computer science course: a study of twelve factors ” (2001) Brenda Cantwell Wilson, Murray State University; Sharon Shrock, Southern Illinois University This study was conducted to determine factors that promote success in an introductory college computer science course. The model included twelve possible predictive factors including math background, attribution for success/failure (luck, effort, difficulty of task, and ability), domain specific self-efficacy, encouragement, comfort level in the course, work style preference, previous programming experience, previous non-programming computer experience, and gender. Subjects included 105 students enrolled in a CS1 introductory computer science course at a midwestern university. The study revealed three predictive factors in the following order of importance: comfort level, math, and attribution to luck for success/failure.

10. “ Teaching objects-first in introductory computer science ” (2003) Stephen Cooper, Saint Joseph's University; Wanda Dann, Ithaca College; Randy Pausch Carnegie Mellon University An objects-first strategy for teaching introductory computer science courses is receiving increased attention from CS educators. In this paper, the authors discuss the challenge of the objects-first strategy and present a new approach that attempts to meet this challenge. The approach is centered on the visualization of objects and their behaviors using a 3D animation environment. Statistical data as well as informal observations are summarized to show evidence of student performance as a result of this approach. A comparison is made of the pedagogical aspects of this new approach with that of other relevant work.

Annual Best Paper Award Announced Today SIGCSE officers also announced the inauguration of an annual SIGCSE Test of Time Award. The first award will be presented at the 2020 SIGCSE Symposium and recognize research publications that have had wide-ranging impact on the field.

About SIGCSE

The Special Interest Group on Computer Science Education of the Association for Computing Machinery (ACM SIGCSE) is a community of approximately 2,600 people who, in addition to their specialization within computing, have a strong interest in the quality of computing education. SIGCSE provides a forum for educators to discuss the problems concerned with the development, implementation, and/or evaluation of computing programs, curricula, and courses, as well as syllabi, laboratories, and other elements of teaching and pedagogy.

ACM, the Association for Computing Machinery , is the world's largest educational and scientific computing society, uniting educators, researchers, and professionals to inspire dialogue, share resources, and address the field's challenges. ACM strengthens the computing profession's collective voice through strong leadership, promotion of the highest standards, and recognition of technical excellence. ACM supports the professional growth of its members by providing opportunities for life-long learning, career development, and professional networking.

Contact: Adrienne Decker 585-475-4653 [email protected]

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Computing Research News

This article is published in the October 2022 issue.

On Undergraduate Research in Computer Science: Tips for shaping successful undergraduate research projects

Note: Khuller was the recipient of the 2020 CRA-E Undergraduate Research Faculty Mentoring Award , which recognizes individual faculty members who have provided exceptional mentorship, undergraduate research experiences and, in parallel, guidance on admission and matriculation of these students to research-focused graduate programs in computing. CRA-E is currently accepting nominations for the 2023 award program .

One of the goals I hope to accomplish with this article is to open the eyes of faculty to the ways in which bright and motivated undergraduates can contribute meaningfully to their research projects and groups. This piece intends to  help educate folks who  have limited experience with undergraduate research or are unsure how to come up with research projects. I hope it helps others learn quickly from the knowledge I have gained over the years.

Exposing undergraduates to research may encourage them to pursue PhDs At the CRA Conference at Snowbird this summer, data was presented that showed that the overall number of PhDs granted in Computer Science (CS) in the US has not changed substantially in the last decade even though undergraduate programs have grown significantly. Meanwhile, the percentage of US students getting PhDs in CS showed a pretty substantial decline from 48%  to 31%. While there are many factors at play–notably a strong job market for undergraduates– I do know from prior discussions with undergraduate students (UGs), that many CS departments also do not make a substantial effort in exposing UGs to research opportunities. Moreover, when I started as a faculty member I too struggled in defining good research projects for undergraduates (they were either too easy or too similar to PhD research topics, and so were likely not appropriate for undergraduates). I think getting UGs excited about research is perhaps the first step to getting them excited to think about getting a PhD as a career option.

Is research by undergraduate students an oxymoron? I will admit that initially I too was skeptical about the possibility and success of true undergraduate research. My own research experiences as an undergraduate were pathetic. As a student often I would hear people say “I am going to the library to do research”. So I too went to the library to do research. Research to me meant finding something in the library that was not in a textbook, understanding it, and telling people about the work.  At that point I thought I had done some research! I never gave much thought to how new material got into journals to begin with.

Talking to a colleague recently – he said “maybe what all UGs do in a chemistry lab is wash test tubes….”.  The truth is that I do not really know what UG research in chemistry looks like.  But the point I wanted to make with this article is that high level UG research in CS is entirely doable. Indeed, in theoretical computer science (TCS) we have witnessed brilliant papers in top conferences by undergraduate students, and I would argue that UG research can be done quite effectively in other areas of computing research as well.

So what should UG research in CS look like? I have advised over 30 undergraduate researchers and based on my experiences, I have a few observations. Most successful research projects involving undergraduates require a lead time of about 18 months before graduation. It usually takes a few months for the student to read the relevant papers, and for us to identify a topic that aligns with the student’s interests and background. I usually expect that students would have taken both an undergraduate level class in algorithm design as well as discrete mathematics. If they can take a graduate level class, that would also be incredibly valuable.

Tips for shaping successful undergraduate research projects Below is my process for defining a successful UG research project. UGs typically have 12-18 months for a research project, not 3-4 years like most Ph.D. students.

• At my first meeting, I ask the students about the different topics they learned about in their Algorithms class and what appealed to them the most.
• Using their answer from bullet #1, I usually spend some time thinking about the right topic for them to work on. The key here is that any paper that the student has to read should not have a long chain of preceding papers that will take them months to get to. Luckily many graph problems as well as combinatorial optimization and scheduling problems lend themselves to easy descriptions. So in a few minutes you can describe the problem.
• The research should be on a topic of significant interest and related to things I have worked on, and one in which I have some intuition about the direction of research and conjectures that might be true and provable with elementary methods.
• I usually treat undergraduates the same way as PhD students, while being aware that they have limited time (a year) as opposed to PhD students who might begin a vaguely defined research project.
• Have them work jointly with a PhD student, if the research is close enough to the PhD students interests and expertise. It’s also a valuable mentoring experience for the PhD student. Simply having a couple of undergrads work on a project jointly can be motivating for both.
• One benefit of tackling hard problems at this stage is that there is no downside. If a student does not make progress, in the worst case they read a few papers and learn some new things. This allows us to work on problems with less pressure than second and third year graduate students are under.

Over the last 25 years, I have had the opportunity to work with a very large number of talented undergraduates –from University of Maryland (UMD) and Northwestern  University, but also many via the NSF funded REU site program (REU CAAR) that  Bill Gasarch (UMD) and I co-ran from 2012-2018. Many of the students I advised, have published the work they did and subsequently received fellowships and admission to top Ph.D programs. Recent graduates are Elissa Redmiles (Ph.D. UMD), Frederic Koehler (Ph.D. MIT) and Riley Murray (Ph.D. Caltech).  I specifically wanted to mention An Zhu (Ph.D. Stanford University) who first opened my eyes to the amazing work that is possible by undergraduates.

About the Author Samir Khuller received his M.S and Ph.D from Cornell University in 1989 and 1990, respectively, under the supervision of Vijay Vazirani. He was the first Elizabeth Stevinson Iribe Chair for CS at the University of Maryland. As chair he led the development of the Brendan Iribe Center for Computer Science and Innovation, a project completed in March 2019. In March 2019, Khuller joined Northwestern University as the Peter and Adrienne Barris Chair for CS.

His research interests are in graph algorithms, discrete optimization, and computational geometry. He has published about 200 journal and conference papers, and several book chapters on these topics. He served on the ESA Steering Committee from 2012-2016 and chaired the 2019 MAPSP Scheduling Workshop, and served on the program committee’s of many top conferences.  From 2018-2021 he was Chair of SIGACT. In 2020, he received the CRA-E Undergraduate Research Mentoring Award and in 2021 he was selected as a Fellow of EATCS.

He received the National Science Foundation’s Career Development Award, several Department Teaching Awards, the Dean’s Teaching Excellence Award and also a CTE-Lilly Teaching Fellowship. In 2003, he and his students were awarded the “Best newcomer paper” award for the ACM PODS Conference. He received the University of Maryland’s Distinguished Scholar Teacher Award in 2007, as well as a Google Research Award and an Amazon Research Award. In 2016, he received the European Symposium on Algorithms inaugural Test of Time Award for his work with Sudipto Guha on Connected Dominating Sets. He graduated at the top of the Computer Science Class from IIT-Kanpur.

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Stanford Online

How to write a compelling statement of purpose for graduate school.

A statement of purpose (SOP) is a critical component of most graduate school applications, and are often required for various types of graduate level programs, including Graduate Certificates and Master’s Degrees .

An SOP offers you the opportunity to showcase your motivations, qualifications, and aspirations to a school’s Office of Admissions. Crafting an effective SOP requires careful planning and attention to detail. Whether you're applying to Stanford or any other institution, here's a guide on how to write a standout statement of purpose that shows how your goals align with the program's expectations.

Understanding the Prompt

A prompt's comprehensive nature offers you the chance to provide a holistic view of your journey, motivations, and aspirations. Be sure to check the websites of any programs you’re applying to, as they often have additional information or suggested frameworks to get you started.

Stanford Master’s Degree

If you are applying to a Stanford master’s degree program , the recommended maximum length for your SOP is 1,000 words and the prompt for the statement of purpose emphasizes several key elements:

• Reasons for applying
• Preparation for the field of study
• Research interests
• Future career plans
• Relevant aspects of your background

Stanford Graduate Certificate

If you are applying to take individual graduate courses or pursue a graduate certificate through Stanford Online, the prompt contains less elements than for the master’s program. This statement of purpose should be brief, as you’re limited to 4000 characters. You should summarize:

• Specific course work on your transcript that meets the course and or certificate prerequisites
• Relevant aspects of your professional experience

Tips for Writing your Statement of Purpose

After you fully understand the prompt for the program you’re applying to, use these tips to guide your writing:

• Be Concise and Focused Most institutions have maximum lengths for words or characters. With limited space, it's important to be concise and focused. Use each word purposefully to convey your message. Ensure that every paragraph adds value and contributes to your overall narrative.
• Start Strong Your opening should be attention-grabbing. Consider sharing a personal anecdote, a relevant quote, or a thought-provoking question that sets the tone for your SOP. Engaging the reader from the beginning can make your statement more memorable.
• Address the Prompt Thoroughly Cover each aspect of the prompt thoroughly, addressing your reasons for applying, your background preparation, your research interests, and your future career plans. Use specific examples to illustrate your points. For instance, if you're applying to a computer science program, discuss projects, coursework, or experiences that highlight your passion and readiness for further study in this field.
• Showcase Fit with the Program Demonstrate a clear understanding of the program you're applying to and explain why it's an ideal fit for your academic and career goals. Highlight specific courses, professors, research opportunities, or unique features of the program that attracted you. This showcases your commitment to the program and demonstrates that you've done your research. You may consider including reasons your presence will benefit the program as your uniqueness may help set you apart from other applicants.
• Highlight Research Interests Discuss your research interests in detail. Explain how your past experiences have shaped your interests and how the program's resources can help you further develop them. Share any relevant research projects you've been a part of and explain their impact on your academic journey. If your program includes a capstone, you may want to include more actionable, compelling examples.
• Connect to Your Future Career Articulate your future career plans and explain how the program will prepare you for success. Whether you plan to pursue academia, industry, or another path, convey how the skills and knowledge gained from the program will contribute to your career trajectory.
• Weave in Personal Background Share aspects of your personal background that are relevant to your journey. This could include challenges you've overcome, experiences that have shaped your perspective, or unique qualities that set you apart. Ensure that these details contribute to your overall narrative and that adding them showcases your qualifications.
• Edit and Proofread After writing your SOP, review it meticulously for grammar, punctuation, and clarity. Typos and errors can detract from the impact of your statement. Consider seeking feedback from mentors, professors, or peers to ensure your SOP effectively conveys your message.
• Tailor for Specific Programs If you're applying to multiple programs, make sure to customize each SOP to align with the specific program's offerings and requirements. Avoid using a generic SOP for all applications, this tends to be very noticeable to admissions.
• Seek Inspiration from Examples If you’re applying to a Stanford Master’s program, the Stanford Graduate Admissions website provides specific guidance on the statement of purpose. Review your program’s recommendations and, if available, consider reading sample SOPs from successful applicants to gather inspiration and insights.

Writing a compelling statement of purpose for graduate school requires thoughtful reflection, careful planning, and clear communication. By addressing the prompt comprehensively, showcasing your fit with the program, and demonstrating your passion and readiness, you can craft an SOP that stands out and may even increase your chances of admission to your desired program. Although it’s far from the only criteria that will be considered in the admissions process, your SOP is your chance to tell your unique story and show why you are a perfect candidate for graduate study. We hope you find this guide useful as you write your statement of purpose, please know that following this guide does not guarantee your admission to any program.

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Department of Computer Science and Engineering

Indian institute of technology, tirupati, research areas.

The following areas are the core and active Research Domains of the Computer Science and Engineering Department of IIT Tirupati

AI Accelerator

An AI accelerator is a class of specialized hardware accelerator or computer system designed to accelerate artificial intelligence applications, especially artificial neural networks, machine vision and machine learning. Typical applications include algorithms for robotics, the internet of things and other data-intensive or sensor-driven tasks. They are often manycore designs and generally focus on low-precision arithmetic, novel dataflow architectures or in-memory computing capability. As of 2018, a typical AI integrated circuit chip contains billions of MOSFET transistors. A number of vendor-specific terms exist for devices in this category, and it is an emerging technology without a dominant design.

The faculties working in this domain are:

Dr Jaynarayan T Tudu

Algorithms and Data Structures

In mathematics and computer science, an algorithm is a finite sequence of well-defined, computer-implementable instructions, typically to solve a class of problems or to perform a computation. Algorithms are always unambiguous and are used as specifications for performing calculations, data processing, automated reasoning, and other tasks. A data structure is a data organization, management, and storage format that enables efficient access and modification. More precisely, a data structure is a collection of data values, the relationships among them, and the functions or operations that can be applied to the data.

Dr G Ramakrishna

Cloud and Edge Computing

Cloud computing is the on-demand availability of computer system resources, especially data storage (cloud storage) and computing power, without direct active management by the user. Edge computing is a distributed computing paradigm that brings computation and data storage closer to the sources of data. This is expected to improve response times and save bandwidth.”A common misconception is that edge and IoT are synonymous. Simply stated, edge computing is a topology- and location-sensitive form of distributed computing, while IoT is a use case instantiation of edge computing.” The term refers to an architecture rather than a specific technology.

Dr Mahendran V

Computational Complexity

The computational complexity of an algorithm is the amount of resources required to run it. Particular focus is given to time and memory requirements. The complexity of a problem is the complexity of the best algorithms that allow solving the problem.

Computer Networks

A computer network is a group of computers that use a set of common communication protocols over digital interconnections for the purpose of sharing resources located on or provided by the network nodes. The interconnections between nodes are formed from a broad spectrum of telecommunication network technologies, based on physically wired, optical, and wireless radio-frequency methods that may be arranged in a variety of network topologies.

Dr Venkata Ramana Badarla

Computer Vision

Computer vision is an interdisciplinary scientific field that deals with how computers can gain high-level understanding from digital images or videos. From the perspective of engineering, it seeks to understand and automate tasks that the human visual system can do.

Dr Sridhar Chimalakonda

Computing for Education

Computing education is the science and art of teaching and learning of computer science, computing and computational thinking. As a subdiscipline of pedagogy it also addresses the wider impact of computer science in society through its intersection with philosophy, psychology, linguistics, natural sciences, and mathematics. In comparison to science education and mathematics education, computer science education is a much younger field. In the history of computing, digital computers were only built from around the 1940s – although computation has been around for centuries since the invention of analog computers.

Data Science Algorithms and Applications

Data science is an interdisciplinary field that uses scientific methods, processes, algorithms and systems to extract knowledge and insights from structured and unstructured data and apply knowledge and actionable insights from data across a broad range of application domains. Data science is related to data mining, machine learning and big data.

Dr Kalidas Yeturu

High Performance Parallel Computing

Parallel computing is a type of computation in which many calculations or processes are carried out simultaneously. Large problems can often be divided into smaller ones, which can then be solved at the same time. There are several different forms of parallel computing: bit-level, instruction-level, data, and task parallelism. Parallelism has long been employed in high-performance computing but has gained broader interest due to the physical constraints preventing frequency scaling. As power consumption (and consequently heat generation) by computers has become a concern in recent years, parallel computing has become the dominant paradigm in computer architecture, mainly in the form of multi-core processors.

Dr Raghavendra K

Internet of things (IoT)

The Internet of things (IoT) describes the network of physical objects “things”— that are embedded with sensors, software, and other technologies for the purpose of connecting and exchanging data with other devices and systems over the Internet. Machine learning (ML) is the study of computer algorithms that improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so. Machine learning algorithms are used in a wide variety of applications, such as in medicine, email filtering, and computer vision, where it is difficult or unfeasible to develop conventional algorithms to perform the needed tasks.

Machine Learning

Machine learning (ML) is the study of computer algorithms that can improve automatically through experience and by the use of data. It is seen as a part of artificial intelligence. Machine learning algorithms build a model based on sample data, known as “training data”, in order to make predictions or decisions without being explicitly programmed to do so.

Reinforcement Learning

Reinforcement learning (RL) is an area of machine learning concerned with how intelligent agents ought to take actions in an environment in order to maximize the notion of cumulative reward. Reinforcement learning is one of three basic machine learning paradigms, alongside supervised learning and unsupervised learning.

Dr Ajin George Joseph

Software Engineering

Software engineering is the systematic application of engineering approaches to the development of software. Software is a collection of instructions and data that tell a computer how to work. This is in contrast to physical hardware, from which the system is built and actually performs the work. In computer science and software engineering, computer software is all information processed by computer systems, including programs and data. Computer software includes computer programs, libraries and related non-executable data, such as online documentation or digital media. Computer hardware and software require each other and neither can be realistically used on its own.

Stochastic Optimization

Stochastic optimization (SO) methods are optimization methods that generate and use random variables. For stochastic problems, the random variables appear in the formulation of the optimization problem itself, which involves random objective functions or random constraints. Stochastic optimization methods also include methods with random iterates. Some stochastic optimization methods use random iterates to solve stochastic problems, combining both meanings of stochastic optimization. Stochastic optimization methods generalize deterministic methods for deterministic problems.

VLSI Test & Verification

Very large-scale integration (VLSI) is the process of creating an integrated circuit (IC) by combining millions of MOS transistors onto a single chip. VLSI began in the 1970s when MOS integrated circuit chips were widely adopted, enabling complex semiconductor and telecommunication technologies to be developed. The microprocessor and memory chips are VLSI devices. Before the introduction of VLSI technology, most ICs had a limited set of functions they could perform. An electronic circuit might consist of a CPU, ROM, RAM and other glue logic. VLSI lets IC designers add all of these into one chip.

Wireless Networks

A wireless network is a computer network that uses wireless data connections between network nodes. Wireless networking is a method by which homes, telecommunications networks and business installations avoid the costly process of introducing cables into a building, or as a connection between various equipment locations. Examples of wireless networks include cell phone networks, wireless local area networks (WLANs), wireless sensor networks, satellite communication networks, and terrestrial microwave networks.

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Computer Science Personal Statement Examples

What is a computer science personal statement?

Your application form features your grades, but the UCAS personal statement is an opportunity to sell youself to the university.

This means you need to include your skills, goals and suitability for the course when drafting a computer science undergraduate or postgraduate personal statement.

Make sure you convey your talents for programming and why you are committed to this course. Read through some of our computer science personal statement examples to see what makes a good and successful statement.

How do I write a computer science personal statement?

When it comes down to how to start a personal statement, don’t tie yourself in knots. Why do you want to study computer science? Personal statements should answer this question, so open with your motivation during your introduction.

Your computer science personal statement should be easy to read, explaining why you have chosen this course and how you intend to work hard to achieve your goals. Give your computer science personal statemen to others to proofread, and ensure the language is concise, makes sense, and is grammatically correct. Don't just rely on a spellchecker for your final draft - read it through yourself, and check for errors thoroughly.

What should I include in my computer science personal statement?

• What subject areas do you enjoy that will support your application? For example, you might pick a topic from your mathematics A level that particularly interests and talk about why you find it fascinating.
• Remember that you can only write one personal statement, so it needs to be suitable for all the universities you are planning on applying to. 
• Talk about your hobbies and extracurricular activities, and how they are relevant - what have you learned from them? Have they inspired you to do anything else? For example, have you built a computer from scratch, or built a new app or website? Are you able to code? If so, what languages can you code in and how did you learn?
• If you’re applying for a postgraduate course, you may want to talk about higher level skills you possess such as innovation, and the results of your final year undergraduate project. 
• Think about your wider reading, e.g. newspapers, magazines, journals, etc. What recent developments interest you, and why are they exciting? Remember, your computer science personal statement needs to stand out from the crowd, so make it as relevant as possible, while giving it your own, unique voice.

How do I write my computer science personal statement introduction?

Try to start your computer science personal statement with a paragraph that will immediately grab the reader's attention. For example, you might relate a story about an experience with computer science when you were a child, such as a birthday present or a day trip with your family. You might also choose to open your statement by talking about one or two aspects of computer science that fascinate you, and why you find them interesting.

For example, this candidate talks about Linux and how they overcame the challenges of using this operating system:

"My views about computing changed considerably when I heard about Linux. In the late nineties it was a newer operating system and tasks like installing and configuring were considered to be quite challenging in India. However, I was intrigued by this challenge and without any formal training I was able to independently install this system. This was due to the sound knowledge I had acquired through reading a vast range of technical books. My fascination towards the evolving IT industry has been growing ever since. "

Not only does this pick out something specific from the world of computing, but shows the reader that the applicant had the persistence and ambition to figure out how to install and use the operating system using textbooks, which is the sort of student they are looking to engage on their course.

Another example is the following candidate, who chose to open their statement by recounting the time they built their first computer:

"Building my first computer was an experience I will never forget. Looking over what seemed to be a city of silicone, I marveled at how elegantly the components were arranged on the motherboard. Yet I did not feel fully satisfied, as I knew there was a whole other world of computing, which could only be explored by completing a degree in computer science.

Studying A Level mathematics has taught me that there are countless methods of approaching a problem and I have also found this to be true of programming."

Again, the student has picked out something specific and told a story, which helps to engage the reader straight away and tells them how interested they are in computing. They then go on to relate their current studies to the course, which is another strength of the opening of this statement.

Hopefully these two examples show how you might put together your own unique opening for your computer science statement, but if you're still struggling, take a look at the rest of our example personal statements .

How do I write a conclusion for my computer science personal statement?

We suggest rounding off your statement with a paragraph about your extracurricular activities and hobbies, and how they relate to your course. For example:

"I also participated at a first-aid national contest organized by the Red Cross Romania, which gave me the opportunity to be the leader of a rescue team. This helped me understand better how to face critical situations and improve my leadership skills.

I often think that computer science will give me the chance to reach higher peaks, and I really consider that it has already helped me see life in a different way. Programming gave me the chance to help many children with special needs, to meet interesting people, to discover a new world. That is exactly why I would like to study and follow a career in this field."

Further resources

For more help and advice on what to write in your computer science personal statement, please see:

• Personal Statement Editing Services
• Personal Statement Tips From A Teacher
• Analysis Of A Personal Statement
• The 15th January UCAS Deadline: 4 Ways To Avoid Missing It
• Personal Statement FAQs
• Personal Statement Timeline
• 10 Top Personal Statement Writing Tips
• What To Do If You Miss The 15th January UCAS Deadline.

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CS Senior Spotlight: Elena Fabian

Fabian will join berkeley law this fall as a member of the inaugural class of berkeley innovation scholars studying the intersection of law, technology, and society.

During her time at Northwestern, Elena Fabian sparked her curiosity and balanced her focus across computer science, engineering, and social sciences through a mix of what she described as paper-problems (technical acumen) and people-problems (user experience).

“My research interests lie in daily technology-use and technology-regulation, whether it's creating embedded solutions for individuals or protecting individuals’ values when they aren't in control of emerging technology,” Fabian said.

An active member of the Northwestern Computer Science community, Fabian was recently named among 12 ‘outstanding CS seniors.’ She served as a peer mentor for COMP_SCI 111: Fundamentals of Computer Programming I for one quarter and supported students in the COMP_SCI 213: Intro to Computer Systems course for six quarters. As a member of Professor Peter Dinda’s Prescience Lab , Fabian collaborated on the Privacy Backplane project, which examines computationally enforced and individualized privacy policy in physical spaces.

Fabian was the treasurer for the McCormick School of Engineering honor society Tau Beta Pi and an executive with the Northwestern chapter of the international women’s fraternity Zeta Tau Alpha . She also volunteered at Women in Computing outreach events at local high schools and helped organize engineering outreach events for middle school students in the Evanston/Skokie School District 65. Fabian was also an editor of the Northwestern Undergraduate Law Journal .

She completed her degree requirements in December and has been working with the Emerging Technologies Advanced Engineering Team at the Chamberlain Group through the CRDV 311-1 Professional Engineering Internship .

We asked Fabian about her experiences at Northwestern Engineering, impactful collaborative experiences, and her advice for current students.

Why did you decide to pursue the CS major at McCormick?

I really enjoyed how cross-disciplinary McCormick seemed and how collaborative I found it to be. I also plan on taking the patent bar which has engineering background admission requirements.

Looking at the degree requirements, I realized I could get exposure to several engineering disciplines, explore many areas of computer science, and balance my interest in social interaction with technology through social sciences and humanities courses.

The project and technical elective requirements were my favorite parts because they allowed me to build depth in the computer systems area and ultimately created a systems community. The community-within-a-community benefited my learning style, allowing me to discuss connections between material across classes as opposed to isolating coursework for a single exam.

How did the McCormick curriculum help build a balanced, whole-brain ecosystem around your studies?

The Design Thinking and Communication and Engineering Analysis classes approached a people-problem and a paper-problem, respectively, and both were incredibly helpful in connecting coursework to my internship experience.

In my most recent internship, a team of computer and electrical engineers, industrial designers, and UI/UX designers developed a product over 16 weeks and produced functional, professionally manufactured models. The paper-problems we looked at assessed the feasibility of all the engineering components, from modeling battery life and communication protocols to adhesives. The people-problem we looked at led us to consider ergonomics, device storage, and charging methods, and most importantly, fulfill a project statement based on user needs.

What are some examples of collaborative or interdisciplinary experiences at Northwestern that were impactful to your education and research?

It’s super cool that I was able to count three interdisciplinary McCormick courses toward my legal studies minor. The Department of Civil and Environmental Engineering offers the CIV_ENV 302-0: Engineering Law course, which is taught by adjunct professor and attorney Patrick Croke . The course exposed me to many aspects of law that impact engineers — from startup structure to liability.

In addition, in the COMP_SCI 397, 497: Innovation Lab: Building Technologies for the Law course — which is cross-listed in Northwester’s Pritzker School of Law and co-taught by Professors Kristian Hammond and Daniel Linna — I was able to see the impact of technology on the legal field. My team of two computer science undergraduates and three law students built a lightweight e-reader for Omnibus bills.

A third impactful class was Assistant Professor of Instruction Sruti Bhagavatula’s COMP_SCI 312, 412: Data Privacy course, where we explored technical, social, and legal definitions and implementations of privacy.

What skills or knowledge did you learn in the undergraduate program that you think will stay with you for a lifetime?

The process of understanding my learning style and refining my interests will absolutely stay with me for a lifetime. I learned that course summaries are my best exam study technique and make for a great reference. Part of that is verbalizing what I know, so I also like doing work with friends whether they're in the same class or not.

During the COVID-19 pandemic and remote classes, I didn't love school, and getting involved really changed that. Peer mentoring and engaging with the CS community helped me remember that I enjoy the material and renewed my curiosity. A big lesson is to try new things until I find what works and to keep those strategies in my back pocket.

What's next? What are your short- and long-term plans/goals in terms of graduate studies and/or your career path?

I plan on spending the next three years at Berkeley Law as a member of the inaugural class of Berkeley Innovation Scholars. I ultimately want to pursue the intersections of technology and law as a patent lawyer to continue seeing emerging technology developed, as well as exploring emerging regulation of ingrained systems such as the Internet and Internet of Things.

What advice do you have for current Northwestern CS students?

My academic advice is to take the core courses early and go to those office hours. Peer mentors are so ready to help with current course content and future course recommendations.

If you can verbalize a concept, or even the blocking question, you are actively understanding the material and courses are so much more interesting when you work through understanding rather than stress through getting stuff done. You may also find even a harder course more pleasant if you consider your needs when creating a schedule.

In general, give yourself the grace to take what you need. When the work gets tough, remind yourself why you're doing it and look for one new thing you've learned or one interesting connection you can make to anything else important to you.

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