Find Academic Supervisors

• Computational Complexity Theory
• Computational Algebra
• Theoretical Computer Science
• +7 more

Pranjal Dutta is an Assistant Professor in the College of Computing and Data Science (CCDS) at Nanyang Technological University (NTU), Singapore, joining in July 2025. He previously served as a Research Fellow at the School of Computing, National University of Singapore (NUS), funded by the National Research Foundation (NRF) Singapore from January 2023 to June 2025. Education: Ph.D. in Computer Science, Chennai Mathematical Institute (CMI), 2018–2022 M.Sc. in Computer Science, Chennai Mathematical Institute (CMI), 2016–2018 B.Sc. in Mathematics and Computer Science, Chennai Mathematical Institute (CMI), 2013–2016 Pranjal's research lies at the heart of theoretical computer science, with a focus on computational complexity and computational algebra. He investigates fundamental questions about the power and limitations of algebraic computation, including derandomization, circuit lower bounds, and the role of approximations in complexity theory. His work bridges abstract mathematics with algorithmic design, particularly in algebraic models of computation. His recent publications reflect a deep engagement with Polynomial Identity Testing (PIT), derandomization, and geometric complexity theory. The articles show a consistent trend toward understanding the structure of algebraic circuits, developing efficient algorithms for special circuit classes, and advancing the theoretical foundations of de-bordering in complexity theory. Scientific Awards: ACM India Doctoral Dissertation Award 2023 Google PhD Fellowship (2018–2022) Simons–Berkeley Research Fellowship (Fall 2025) Pranjal has been supported by competitive fellowships and grants, including the Google PhD Fellowship and NRF funding during his postdoc. He advises and mentors students in theoretical computer science, though no current advisees are listed. He is an active peer reviewer, currently handling one journal and one conference paper. He will be affiliated with the Simons Institute at UC Berkeley in Fall 2025 as a Jane Street Research Fellow, participating in the program on Complexity and Linear Algebra, indicating strong collaboration with leading international research teams.

• Nonblocking Algorithms
• Concurrent Data Structures
• Memory Reclamation
• +3 more

Ruslan Nikolaev is an Assistant Professor in the Department of Computer Science and Engineering, specializing in nonblocking algorithms, memory reclamation, and concurrent data structures. His research spans operating systems, virtualization, and lock-free programming paradigms. Academic Rank: Assistant Professor Department: Computer Science and Engineering Research Interests focus on: Nonblocking and wait-free algorithms Memory-efficient reclamation techniques Lock-free data structures Operating system virtualization Device driver security Recent work includes 2025 advancements in SCOT and RRR-SMR frameworks for nonblocking structures, 2024 contributions to wait-free reclamation, and 2022 innovations in address space randomization (Adelie), critical service domains (Kite), and wait-free queues (Wcq). Earlier projects (2013-2020) explored OS virtualization (VirtuOS) and reclamation mechanisms (LibrettOS). Trends in his publications highlight: Optimization of concurrent algorithms Security enhancements in Linux drivers Virtualization techniques for critical services Efficient memory management strategies Robust fault tolerance in distributed systems

Marco Leandro CarmosinoView profile

Adjunct Assistant Professor

• Computational Complexity Theory
• Cryptography
• Theoretical Computer Science
• +3 more

Marco Leandro Carmosino is an Adjunct Assistant Professor at Boston University and a Research Scientist at IBM Yorktown. He holds a PhD from the University of California, San Diego (2019), advised by Russell Impagliazzo. His research focuses on computational complexity theory , exploring the interplay between computational limitations, algorithmic structure, and applications to cryptography and privacy. Key areas include understanding how complexity lower bounds relate to learning algorithms, simulating natural phenomena computationally, and enforcing privacy in machine learning tasks. He has received the Best Paper Award at CCC 2016 for his work linking natural proofs to learning algorithms. His publications span topics from fine-grained derandomization to privacy-preserving learning techniques, with applications in both theoretical computer science and interdisciplinary fields like spectroscopy analysis. Affiliations: Boston University (Adjunct), IBM Yorktown (Research Scientist) Education: PhD in Computer Science, UC San Diego (2019) Key Awards: Best Paper at CCC 2016 His work bridges foundational complexity theory with practical algorithm design, emphasizing privacy and efficiency in computational systems. He contributes to both academic research and industry applications, leveraging theoretical insights to address real-world challenges in security and data analysis.

• Graph Algorithms
• Convex Optimization
• Fine-Grained Complexity Theory
• +3 more

Rasmus Kyng is an Assistant Professor at the Department of Computer Science, ETH Zurich, since Fall 2019. He focuses on developing fast algorithms for graph problems, convex optimization, and structured linear equations, with a specialization in fine-grained complexity theory and dynamic graph algorithms. Assistant Professor, Department of Computer Science, ETH Zurich (2019–present) Postdoc, Theory of Computation Group, Harvard (2018–2019) Research Fellow, Simons Institute, UC Berkeley (Fall 2017) His research explores the intersection of graph algorithms, optimization, and complexity, particularly through dynamic graph data structures, sparsification, and applications in machine learning. Recent work includes breakthroughs in almost-linear time algorithms for network flow problems. Key trends in his publications include: Advancements in dynamic graph algorithms and data structures Applications of convex optimization to graph theory Connections between matrix theory and algorithmic efficiency Derandomization techniques for algorithmic speed Expander graph-based oblivious routing Sparsification and low-congestion vertex sparsifiers Scientific recognition includes the FOCS Best Paper Award (2022), ICBS Frontiers of Science Award (2022), and the Machtey Award for Best Student Paper (2017). His group mentors PhD candidates and postdocs, including Ming Ding , Federico Soldà , Simon Meierhans , Aurelio Sulser , and Wuwei Yuan . Research is supported by Swiss National Science Foundation grants (project no. 200021 204787, starting grant no. TMSGI2 218022).

V. ArvindView profile

Professor

• Computational Complexity Theory
• Structural Complexity
• Randomized Computation
• +5 more

V. Arvind is a Professor in the Theoretical Computer Science faculty at the Institute of Mathematical Sciences (IMSc) , Chennai. His research is centered on computational complexity theory, with a focus on structural complexity, randomized and algebraic computation, and quantum information and computation. He explores the deep connections between theoretical computer science and mathematics. Institution: Institute of Mathematical Sciences (IMSc), Chennai School: Theoretical Computer Science Academic Rank: Professor Arvind's research interests include computational complexity, structural complexity theory, algebraic computation, derandomization, and quantum computing. He is particularly interested in the interplay between mathematical structures and computation. His work often bridges theoretical computer science with algebra, combinatorics, and logic. His recent publications, primarily expository articles in the EATCS Bulletin’s Computational Complexity Column, cover a wide range of topics such as robust oracle machines, the Alon-Roichman theorem, noncommutative arithmetic circuits, graph isomorphism, and quantum computation. These works reflect trends in foundational complexity theory, algebraic methods in computation, and the exploration of quantum models. The articles emphasize structural insights, lower bounds, and connections to mathematical disciplines. Professional Service and Editorial Roles: Associate Editor, ACM Transactions on Computation Theory Editor, EATCS Computational Complexity Column (since June 2011) Editorial Board Member, International Journal of Computer Mathematics (2009–2013) Co-organizer, ICM Satellite Conference on Algebraic and Probabilistic Aspects of Combinatorics and Computing Program Committee Member for WALCOM 2014, STACS 2012, COCOON 2009, FSTTCS (multiple years, including chair roles), CCC 2006, INDOCRYPT (2002, 2005), and others Teaching: Arvind has taught advanced courses including Computational Complexity, Algorithms, Algebra and Computation, and Discrete Mathematics, often based on foundational texts and notes from leading experts. Lecture notes from his courses have been compiled by students and collaborators. Collaborations: He has an extensive list of co-authors, including prominent researchers such as Manindra Agrawal, Eric Allender, Johannes Köbler, Meena Mahajan, Jacobo Torán, and Ramprasad Saptharishi, indicating strong collaborative research networks in complexity theory and algorithms.

• Computational Complexity
• Circuit Complexity
• Randomness in Computation
• +2 more

Dr. Zhenjian Lu is an Assistant Professor in the Department of Computer Science at the University of Victoria, Canada. He previously held postdoctoral positions at the University of Warwick and the University of Oxford, and served as a Research Fellow for the Meta-Complexity program at the Simons Institute. Ph.D. in Computer Science from Simon Fraser University (advised by Valentine Kabanets and Andrei Bulatov) B.Sc. in Computer Science from the University of British Columbia His research focuses on computational complexity theory, including circuit complexity, randomness in computation, meta-complexity, and the interplay between lower bounds and algorithms. Recent work explores one-way functions, Kolmogorov complexity, comparator circuits, and pseudodeterministic constructions. Selected publications highlight advancements in time-bounded Kolmogorov complexity, coding theorems, and derandomization techniques. His research often bridges theoretical foundations with practical algorithmic applications. Contact: zhenjianlu@uvic.ca | Office: ECS 626 | Phone: 250-472-5731

Termeh KoushaView profile

Adjunct Professor

• Environmental Health
• Public Health
• Biostatistics
• +3 more

Termeh Kousha is an Adjunct Professor in the Department of Mathematics and Statistics at the University of Ottawa’s Faculty of Science. Their research focuses on the intersection of environmental health and statistical modeling, particularly examining the impact of air pollution on emergency healthcare utilization. They specialize in applying advanced statistical methods to analyze environmental health risks and public health outcomes. Key research areas include air pollution exposure effects on mental health, respiratory diseases, skin conditions, and cardiovascular health, often employing case-crossover study designs. Their work bridges mathematical rigor—such as convex geometry and algorithmic optimization—with real-world public health challenges. Publications highlight trends in linking air quality indices (e.g., AQHI) to emergency department visits for conditions like asthma, otitis media, and hypertension. Methodological contributions include derandomization of computational algorithms and factorization techniques for environmental pollutant source analysis. No scientific awards or student advisees are explicitly listed. Research has been applied in Canadian cities such as Montreal, Edmonton, Calgary, and Windsor, emphasizing regional health disparities and urban environmental challenges.

• Theoretical Computer Science
• Machine Learning
• Algorithms
• +4 more

Nicholas J. A. Harvey is a Professor of Computer Science at the University of British Columbia. His research spans multiple areas of theoretical computer science with significant contributions to algorithms, machine learning theory, and network coding. Harvey's research focuses on the theoretical foundations of computer science with particular emphasis on: Theory and analysis of randomized algorithms Machine learning theory, including online learning and neural networks Network coding and information theory Combinatorial optimization and graph algorithms High-dimensional probability and statistics His work combines deep mathematical insights with practical applications in computer systems and networking. Analysis of Harvey's recent publications (2022-2025) reveals a strong focus on theoretical aspects of machine learning and optimization. His work spans continuous prediction with experts' advice, tight analyses of optimization algorithms like subgradient descent, and fundamental questions in randomized algorithms. A significant portion of his recent research addresses challenges in high-dimensional statistics, privacy-preserving estimation, and graph neural networks. His publications demonstrate a consistent pattern of addressing foundational theoretical questions with implications for practical machine learning systems. Harvey has maintained a strong publication record with an h-index of 35 and over 6,000 citations according to Google Scholar metrics. His influential papers include 'SkipNet: A Scalable Overlay Network with Practical Locality Properties' (2003), 'On the capacity of information networks' (2006), and 'Nearly-tight VC-dimension and pseudodimension bounds for piecewise linear neural networks' (2019). Harvey has supervised numerous graduate students and postdoctoral researchers, contributing to the development of the next generation of theoretical computer scientists. His research has been supported by various grants from funding agencies including the Natural Sciences and Engineering Research Council of Canada (NSERC) and collaborations with industry partners. While specific details about laboratory facilities are not available in the provided information, Professor Harvey appears to be part of the theoretical computer science research group at the University of British Columbia, collaborating with researchers across multiple institutions on fundamental problems in algorithms and machine learning theory.

Sebastian ForsterView profile

Professor

• Algorithms
• Graph Theory
• Network Science
• +4 more

Sebastian Forster is a Professor at the University of Salzburg , affiliated with the Faculty of Digital & Analytical Sciences and the Department of Computer Science . He leads the Big Data Algorithms Group and holds administrative roles as Vice Dean, Local Coordinator of CIVIS Hub 5, and coordinator of the supplementary study programme "Informatikkompetenz für alle." His research focuses on algorithms for big and fast data, particularly distributed and dynamic algorithms applied to graphs, networks, and complexity theory. Education: Ph.D. in Computer Science (University of Vienna, 2011–2015) M.Sc. in Computational Intelligence (Vienna University of Technology, 2008–2011) B.Sc. in Computer Science (University of Passau, 2005–2008) Research Agenda: Forster's work addresses the theoretical and mathematical challenges of prior-free algorithms in the post-Moore's Law era. His key contributions include advancements in graph algorithms , fine-grained complexity , and network science . Recent publications span distributed Laplacian methods , dynamic graph sparsifiers , and approximate shortest path solutions , reflecting a blend of algorithmic design and hardness analysis. Scientific Awards: Heinz Zemanek Award (OCG) Award of Excellence (BMWFW) Academic Service: He has served on program committees for ICALP 2025, STACS 2024, FOCS 2023 , and other major conferences. He actively reviews for journals like Journal of the ACM and funding bodies such as BSF and ISF . Co-organized the Dagstuhl Seminar 22461 on Dynamic Graph Algorithms.

• Mathematical Statistics
• Probability Theory
• Learning Theory
• +7 more

Nikita Zhivotovskiy is a tenure-track Assistant Professor in the Department of Statistics at the University of California, Berkeley. He previously held postdoctoral positions at ETH Zürich and Google Research, Zürich, and was affiliated with the Technion I.I.T. His academic background includes a PhD from the Moscow Institute of Physics and Technology, with affiliations during his studies at the Institute for Information Transmission Problems, Higher School of Economics, and Skoltech. His research lies at the intersection of mathematical statistics, probability theory, and learning theory . Key areas include robust estimation, online learning, statistical learning theory, algorithmic stability, and high-dimensional statistics. His work emphasizes theoretical foundations of machine learning, with a focus on generalization, risk bounds, and learning under non-standard assumptions. The recent publications reflect a strong trend in theoretical machine learning , particularly in understanding the limits and optimality of learning algorithms. Topics span PAC learning, online classification, private estimation, and clustering, often achieving dimension-free or high-probability guarantees. His work frequently appears in top venues such as NeurIPS, COLT, and FOCS, indicating significant impact in the field. Scientific Awards: Best Paper Award at Conference on Learning Theory (COLT), 2020 Nikita Zhivotovskiy has served as a reviewer for leading journals including Annals of Statistics , Probability Theory and Related Fields , and IEEE Transactions on Information Theory , and as a senior program committee member for COLT and ALT. He has co-taught courses at ETH Zürich and has advised or collaborated with numerous researchers, though formal students are not listed. His research has been supported through academic and industrial collaborations, including at Google Research. His work is embedded within the theoretical machine learning community, with active participation in workshops such as those at BIRS, and a growing body of work that bridges statistical theory and algorithmic design. While no formal lab is mentioned, his research group at UC Berkeley likely focuses on foundational aspects of learning and inference.

Zhimei RenView profile

Assistant Professor

• Distribution-free inference
• Multiple hypothesis testing
• Data-driven decision making
• +5 more

Zhimei Ren is an Assistant Professor in the Department of Statistics and Data Science at the Wharton School, University of Pennsylvania. She received her Ph.D. in Statistics from Stanford University in 2021 under the supervision of Professor Emmanuel Candès and completed a postdoctoral fellowship at the University of Chicago from 2021-2023, advised by Professor Rina Foygel Barber. Prior to her doctoral studies, she earned a B.S. in Statistics from Peking University in 2016. Her educational background includes: Ph.D. in Statistics, Stanford University, 2021 (Advisor: Emmanuel Candès) B.S. in Statistics, Peking University, 2016 Dr. Ren's research focuses on developing distribution-free statistical methods with applications across various domains. Her work centers on distribution-free inference, multiple hypothesis testing, data-driven decision making, causal inference, and survival analysis. She has made significant contributions to conformal prediction frameworks, developing novel approaches for selection-conditional coverage, interactive framework for conformal selection, and applications in survival analysis. Her research bridges theoretical statistics with practical applications in healthcare, education technology, and machine learning model validation. Analysis of her publication record reveals a strong focus on conformal prediction methods and their extensions. Over the past two years, she has published extensively on topics including conformal selection frameworks, distribution-free inference for survival analysis, and applications of conformal methods to foundation models and time series data. Her work demonstrates a consistent theme of developing theoretically sound statistical methods that provide rigorous guarantees without distributional assumptions, making them broadly applicable across scientific domains. Her notable scientific achievements include: Rising Star in Data Science, University of Chicago, 2022 Waterloo Student Conference Presentation Award, University of Waterloo, 2021 First Prize of the 2018 Citadel Datathon, Stanford University, 2018 Dr. Ren is actively engaged in academic service, currently co-organizing the International Seminar on Selective Inference with prominent researchers in the field. She teaches advanced statistical courses including Statistical Inference (STAT4310) and Seminar in Advanced Applications of Statistics (STAT9910) at the University of Pennsylvania. Her teaching focuses on statistical methodology and inference, preparing students for both theoretical and applied work in statistics and data science. Dr. Ren has also developed several R packages including cfsurvival for conformalized survival analysis and derandomKnock for implementing derandomized knockoffs procedures, demonstrating her commitment to making her research accessible and usable by the broader scientific community.

Guy EvenView profile

Researcher

• Computer Science
• Mathematics
• Approximation Algorithms
• +4 more

Guy Even is a prominent researcher in Computer Science and Mathematics, with significant contributions to approximation algorithms, graph theory, and network design. His work spans theoretical and applied domains, including algorithmic complexity, Steiner tree problems, and IEEE floating-point arithmetic optimization. Research Interests: His research focuses on combinatorial optimization, generalized connectivity in graphs, and efficient algorithm design. He has advanced methodologies for divide-and-conquer strategies, feedback set approximation, and spreading metrics in undirected and directed graph problems. Key Article Trends: His publications from 1992 to 2013 highlight advancements in approximation algorithms for NP-hard problems, with a particular emphasis on Steiner networks (undirected/directed), Boolean network observability, and hardware-efficient rounding techniques for floating-point arithmetic. These works bridge theoretical insights with practical applications in computing. Collaborations: Guy has collaborated with notable researchers such as Chandra Chekuri, Anupam Gupta, and Danny Segev. His co-authorship network includes Moti Medina (71 publications), Nissim Halabi (14 publications), and other leading scholars in algorithmic and computational fields. Notable Affiliations: While specific institutions are not explicitly stated in the provided text, his work is cited in prestigious venues like ACM, IEEE, and Springer, indicating strong ties to academia and research communities.

• Graph Algorithms
• Distributed Algorithms
• Parallel Algorithms
• +3 more

Dr. Peter Davies-Peck is an Assistant Professor in the Department of Computer Science at Durham University. His research focuses on distributed algorithms, graph theory, and parallel computing. He has held roles on programme committees for major conferences like PODC and ICDCS, and has been an invited speaker at workshops associated with DISC. His research interests include Graph Algorithms, Distributed Algorithms, Randomised Algorithms, and Communications Networks. Notable work involves applying the Lovász Local Lemma to distributed computing challenges and developing efficient message-passing protocols in noisy environments. Recent contributions include advancements in parallel derandomization for graph coloring, optimal message-passing in radio networks, and distributed mean estimation techniques. His work bridges theoretical algorithm design with practical distributed system challenges, emphasizing scalability and resilience. Esteem indicators include Programme Committee membership for PODC 2023, ALGOSENSORS 2022, and ICDCS 2021. His publications span top venues like STOC, SODA, and the Journal of the ACM, reflecting impactful contributions to theoretical computer science and distributed systems.

Jeffrey NegreaView profile

Assistant Professor

• Machine Learning
• Statistics
• Artificial Intelligence
• +3 more

Jeffrey Negrea is an Assistant Professor at the University of Waterloo. His research focuses on foundational aspects of machine learning, statistics, and optimization, with a particular emphasis on stochastic algorithms, Markov chain theory, and adversarial analysis. His work bridges theoretical computer science and applied statistics, addressing challenges in algorithmic convergence, generalization bounds, and scalable inference methods. Key research directions include developing robust methods for online learning, analyzing stochastic iterative algorithms through scaling limits, and exploring the interplay between information theory and machine learning generalization. He has contributed to advancements in Thompson Sampling for adversarial environments, minimax regret optimization, and the theoretical underpinnings of MCMC and approximate inference techniques. His publications span topics from adversarial analysis of Thompson Sampling to quantitative error bounds for stochastic processes. While no formal awards or grants are explicitly listed in the provided data, his active publication record reflects ongoing contributions to computational statistics and machine learning theory. No student advising or lab affiliations are mentioned in the available information.

Qi ChengView profile

Professor

• Theoretical Computer Science
• Cryptography
• Computational Number Theory
• +3 more

Qi Cheng is the Williams Company Foundation Presidential Professor of Computer Science at the University of Oklahoma, where he has been since 2001. His academic roles include leadership in theoretical computer science research and teaching. Education: Ph.D., Computer Science, University of Southern California (2001) M.S., Computer Science, Fudan University (1995) B.S., Computer Science, Nankai University (1992) Research Interests: Focuses on theoretical computer science, cryptography, computational number theory, DNA/molecular computing, and coding theory. His work explores algorithmic self-assembly, lattice-based cryptography, and polynomial system solving over finite fields. Key Contributions: Includes advancements in self-assembly theory, decoding algorithms for Reed-Solomon codes, and complexity analyses of algebraic problems. His NSF CAREER project integrated self-assembly research with educational initiatives. Grants & Awards: Recipient of the NSF Career Award (2003–2008) for self-assembly research, and multiple NSF grants (e.g., CCF-1900820, CCF-1409294) supporting work on sparse polynomials and algebraic codes. Labs/Teams: Leads research groups focused on theoretical computer science and cryptography, collaborating on projects involving post-quantum cryptography and algorithmic number theory.