Find Academic Supervisors

• Analysis of Algorithms
• Data Analysis
• Massive Data Sets
• +15 more

Robert Krauthgamer is the Harry Weinrebe Professor of Computer Science and currently serves as Department Head in the Department of Computer Science & Applied Mathematics at the Weizmann Institute of Science , within the Faculty of Mathematics and Computer Science . He is a leading researcher in theoretical computer science, particularly in the analysis of algorithms. Research Interests: His research focuses on Analysis of Algorithms , with deep expertise in Data Analysis and Massive Data Sets , Combinatorial Optimization , Approximation Algorithms , Hardness of Approximation , Embeddings of Finite Metrics , and Routing and Peer to Peer Networks . He also maintains a broad interest in Discrete Mathematics and High-Dimensional Geometry . His recent publications highlight work in graph algorithms, parameterized complexity, streaming algorithms, and metric embeddings. Publication Trends: His most recent work, including papers from SODA 2016, demonstrates a strong trend in the design and analysis of efficient algorithms for fundamental problems in graph theory, optimization, and data streams. Key themes include kernelization and sampling techniques for dynamic graph streams, subexponential parameterized algorithms, deterministic derandomization of the polynomial method, and structural results for graph modification problems. His research often bridges theoretical insights with applications in computational biology and network science. Service and Recognition: Journal Editorial: Editor-in-Chief of SIAM Journal on Computing (2019–2025), Associate Editor (2012–2017); Managing Editor of Theory of Computing (2007–2018), and current Editorial Board Member. Conference Leadership: Program Committee Chair for SODA 2016 and HALG 2018; Steering Committee member for SODA, ESA, and HALG; and committee member for the Gödel Prize (2019–2021). Workshops: Organizer of numerous workshops on sublinear algorithms, fine-grained complexity, and high-dimensional data. Teaching and Mentorship: He regularly teaches advanced courses such as Randomized Algorithms and Sublinear Time and Space Algorithms . He advises a large group of MSc and PhD students and hosts postdoctoral researchers, demonstrating a strong commitment to training the next generation of computer scientists. His former students have gone on to successful academic and research careers. Laboratories and Research Groups: He is a key member of the Foundations of Computer Science (theory) seminar at Weizmann and has organized the TheoryLunch and Reading Group in Algorithms, fostering a vibrant research community within the department.

Heng GuoView profile

Associate Professor

• Algorithms
• Computational Complexity
• Approximate Counting
• +5 more

Heng Guo is an Associate Professor in Algorithms and Complexity at the School of Informatics, University of Edinburgh. He leads the ERC starting grant project New Approaches to Counting and Sampling (NACS), which runs from 2021 to 2026. Previously, he has worked and studied at Berkeley, London, Madison, and Beijing. His research lies at the intersection of theoretical computer science, combinatorics, and statistical physics. Guo's research focuses on algorithms from a complexity perspective, particularly computational counting and sampling. He is renowned for his work on the Lovász local lemma, Markov chain Monte Carlo methods, phase transitions in computational complexity, and complexity classifications. His approach often involves discovering unseen links between different areas of theoretical computer science. Key contributions include confirming a conjecture of Gorodezky and Pak through partial rejection sampling, establishing rapid mixing for Swendsen-Wang dynamics, and developing a polynomial-time approximation algorithm for all-terminal network reliability. His publication record shows a strong trajectory of impactful research in top venues including FOCS, STOC, SODA, J. ACM, and SIAM Journal on Computing. His work on the all-terminal network reliability problem won the Best Paper Award at ICALP 2018. Guo has organized several significant workshops including JerrumFest 2025, MCMC 2.0 (Shonan seminar), and a STOC 2020 workshop on new frontiers of approximate counting. These events highlight his leadership role in the theoretical computer science community. Best Paper Award at ICALP 2018 EATCS Distinguished Dissertation Award 2016 Guo has advised several PhD students including Giorgos Mousa, Jiaheng Wang, and Graham Freifeld, and mentored postdocs such as Weiming Feng, Vishvajeet Nagargoje, and Konrad Anand. His ERC grant supports multiple research associates working on counting and sampling problems. He has taught courses including Computational Complexity, Randomness and Computation, and Algorithmic Game Theory at the University of Edinburgh.

• Tropical Geometry
• Convex Optimization
• Algorithmic Game Theory
• +7 more

Mateusz Skomra is a CNRS researcher currently based at the Laboratoire d'analyse et d'architecture des systèmes (LAAS) in Toulouse, France, where he is a member of the POP research team. Previously, he held postdoctoral positions at École normale supérieure de Lyon and CNRS within the Laboratoire de l'Informatique du Parallélisme and the MC2 team. He earned his PhD from École polytechnique, Centre de Mathématiques Appliquées, under the supervision of Xavier Allamigeon and Stéphane Gaubert, as part of the INRIA Tropical team and Université Paris-Saclay. His research lies at the intersection of tropical geometry, convex optimization, algorithmic game theory, and algebraic complexity. He has made significant contributions to the understanding of tropical convexity, semidefinite programming over nonarchimedean fields, mean payoff and stochastic games, and algebraic reconstruction problems. His work often bridges theoretical mathematics with computational aspects, particularly in optimization and complexity theory. The analysis of his recent publications reveals a consistent focus on tropical methods in optimization and game theory, with increasing attention to algorithmic and computational implications. His work connects deep algebraic structures with practical algorithm design, especially in deriving complexity bounds and developing solution methods for games and polynomial systems. Key recurring themes include value iteration, derandomization, Minkowski sums, and the interplay between geometry and algebra in computational settings. Prix Dodu for best presentation by a young researcher Mateusz Skomra has advised doctoral research during his PhD under leading experts and continues to mentor students through master’s internships, such as one on analyzing mean payoff games using sums-of-squares techniques. While specific grant details are not listed, his affiliations with CNRS, INRIA, and participation in numerous national and international workshops suggest active involvement in collaborative research projects. He frequently contributes to academic discourse through seminars and conference presentations across Europe. He is actively involved in research teams including the POP team at LAAS-CNRS, previously the MC2 team at ENS Lyon and the Tropical team at INRIA. These teams focus on mathematical computing, optimization, and theoretical computer science, providing a rich interdisciplinary environment for his work.

Zeyu GuoView profile

Assistant Professor

• Theoretical Computer Science
• Computational Complexity
• Pseudorandomness
• +3 more

Zeyu Guo is an Assistant Professor in the Department of Computer Science and Engineering at The Ohio State University , based in Dreese Laboratories, Columbus, Ohio. He earned his Ph.D. in Computer Science from the California Institute of Technology in 2017 under the supervision of Chris Umans, followed by postdoctoral appointments at the University of Texas at Austin, the University of Haifa (Israel), and the Indian Institute of Technology Kanpur (India). Research Interests Theoretical Computer Science Computational Complexity Pseudorandomness and Derandomization Coding Theory Algebraic Complexity Theory Algebraic Algorithms His work explores deep interplay between algebra and computation, designing efficient algorithms, constructing pseudorandom objects, and establishing fundamental limits in error-correcting codes. Recent Publication Landscape Across 2021-2025, Guo’s papers predominantly target list decoding capacity for Reed–Solomon and Gabidulin codes, polynomial identity testing for restricted circuit classes, and fast algebraic algorithms such as multivariate multipoint evaluation. A recurring theme is leveraging algebraic geometry and additive combinatorics to derandomize constructions and achieve optimal parameters. Awards & Funding NSF CAREER Award (current support) Advising & Mentoring PhD student: Zihan Zhang Postdoc mentee: Ashish Dwivedi (2023–2024) Teaching & Course Development Guo regularly teaches core graduate and undergraduate courses including Algorithms, Computability and Complexity, and specialized topics in algebraic complexity theory and error-correcting codes.

• Theoretical computer science
• Randomness and Pseudorandomness
• Communication Complexity
• +3 more

Pooya Hatami is an Associate Professor in the Department of Computer Science and Engineering at Ohio State University's College of Engineering. He joined the CSE department in 2019 after completing postdoctoral research at UT Austin (hosted by David Zuckerman) and spending two years as a postdoctoral researcher at the Institute for Advanced Study at Princeton and DIMACS at Rutgers University. He earned his Ph.D. from the University of Chicago in 2015 under the supervision of Alexander Razborov and Madhur Tulsiani. His research interests focus on Theoretical computer science , with particular emphasis on Randomness and Pseudorandomness, Communication Complexity, Analysis of Boolean Functions, Additive Combinatorics, and Learning Theory . His work often bridges theoretical computer science with mathematical concepts, developing rigorous frameworks for understanding computational complexity and designing efficient algorithms. His research has been supported by NSF grant CCF-1947546, demonstrating the significance and impact of his contributions to the field. An analysis of his recent publications reveals a strong focus on the intersection of learning theory and computational complexity, with particular attention to replicability in machine learning, communication complexity lower bounds, and the theoretical foundations of Boolean functions. His work on the Implicit Graph Conjecture and Borsuk-Ulam applications to learning theory demonstrates innovative approaches to longstanding problems in theoretical computer science. Scientific Awards: Best Paper Award at ICALP 2023 for "Online Learning and Disambiguations of Partial Concept Classes" Professor Hatami actively mentors graduate students, currently advising Pushen Wang and Yuting Fang in Computer Science and Engineering, and Chavdar Lalov and Sivan Tretiak in Mathematics. His involvement in program committees for major conferences like SODA 2024 and FOCS 2025 highlights his standing in the theoretical computer science community. His research has established important connections between pseudorandomness, communication complexity, and learning theory, contributing significantly to our understanding of fundamental computational limits.

• Cryptography
• Computational Complexity
• Pseudorandomness
• +5 more

Andrej Bogdanov is a Professor in the Department of Computer Science at the Weizmann Institute of Science's Faculty of Mathematics and Computer Science. With a prolific publication record spanning over two decades from 2002 to 2025, he has established himself as a leading researcher in theoretical computer science and cryptography. His research interests span multiple areas of theoretical computer science, with a particular focus on cryptography, computational complexity, pseudorandomness, and secret sharing. His work often bridges theoretical foundations with practical cryptographic applications, exploring the mathematical underpinnings of secure computation and cryptographic primitives. His research has evolved to address contemporary challenges in quantum computing security and machine learning evaluation. Bogdanov's publication record shows consistent contributions to top-tier conferences including FOCS, STOC, CRYPTO, TCC, and ITCS. His work demonstrates deep theoretical insights while maintaining relevance to practical cryptographic applications. Recent publications indicate expanding interests into quantum computing security and machine learning evaluation frameworks. Bogdanov has collaborated extensively with leading researchers in theoretical computer science, most notably with Alon Rosen (31 joint publications), as well as Siyao Guo, Yuval Ishai, and Chin Ho Lee. His collaborative work spans multiple institutions and reflects the interdisciplinary nature of modern theoretical computer science research. His academic contributions include foundational work on pseudorandom generators, secret sharing schemes, hardness amplification, and more recently, contributions to post-quantum cryptography and quantum security. His research has been supported by multiple grants that have enabled his team to explore the theoretical boundaries of cryptographic security.

Richard GuoView profile

Assistant Professor

• Replicable data analysis
• Causal inference
• Uncertainty quantification
• +3 more

Richard Guo is an Assistant Professor of Statistics at the University of Michigan, Ann Arbor. He holds a PhD in Statistics from the University of Washington (2021) and an MS in Computer Science from Duke University (2016). His research focuses on replicable data analysis, causal inference foundations, and uncertainty quantification. Guo previously served as a Research Associate at the University of Cambridge and a Richard M. Karp Fellow at the Simons Institute. He has developed software tools like the MultiSplit R package for hypothesis testing with multiple data splits. His awards include the Richard M. Karp Research Fellowship (2022) and the Z. W. Birnbaum Award (2019–2020). Education: PhD in Statistics, University of Washington, 2021 MS in Computer Science, Duke University, 2016 BEng in Computer Science, University of Electronic Science and Technology of China, 2013 Research Interests: Replicable data analysis: randomized procedures and derandomization Causal inference: graphical models, nonparametric methods Uncertainty quantification: model selection, finite-sample guarantees Awards: Richard M. Karp Research Fellowship, Simons Institute (2022) Z. W. Birnbaum Award, University of Washington (2019–2020) Teaching: Instructor for graduate courses on linear models and probability theory at University of Washington Guest lectures on causal DAGs and statistical modeling

Constantine CaramanisView profile

Professor

• Optimization
• Machine Learning
• High-Dimensional Statistics
• +2 more

Constantine Caramanis is a Professor and holder of the Chandra Family Endowed Distinguished Professorship in Electrical and Computer Engineering at the University of Texas at Austin. His research focuses on decision-making in large-scale complex systems, specializing in robust optimization, high-dimensional statistics, machine learning, and applications to networks including social, wireless, transportation, and energy systems. Caramanis directs a substantial research group and is affiliated with the NSF Institute for Foundations of Machine Learning and the UT Machine Learning Lab. He has made significant contributions to optimization theory, developing efficient algorithms for matrix sensing, non-convex problems, and combinatorial optimization. His recent work bridges machine learning and control theory through latent MDPs, diffusion models for inverse problems, and bandit algorithms with fairness considerations. Caramanis has received prestigious recognition including the NSF CAREER award and IEEE Fellowship for his theoretical advances in optimization and learning.

Zhimei RenView profile

Assistant Professor

• Statistical Inference
• Survival Analysis
• Multiple Testing
• +1 more

Zhimei Ren is an Assistant Professor in the Department of Statistics and Data Science at the Wharton School, University of Pennsylvania. She completed her PhD in Statistics at Stanford University in 2021 under the supervision of Emmanuel Candès, with committee members Trevor Hastie, Art Owen, and Jonathan Taylor. Her dissertation focused on model-free methods for multiple testing and predictive inference. She previously served as a postdoctoral researcher at the University of Chicago Statistics Department (2021-2023) under Rina Foygel Barber. She co-organizes the International Seminar on Selective Inference and maintains active research in conformal inference, derandomized knockoffs, and robust statistical methods.

Chris UmansView profile

Professor

• Theoretical Computer Science
• Computational Complexity
• Derandomization
• +4 more

Chris Umans is a Professor of Computer Science in the Computing and Mathematical Sciences department at the California Institute of Technology (Caltech), affiliated with the Theory Group. He earned his Ph.D. from UC Berkeley in 2000 and joined Caltech in 2002 after a postdoc at Microsoft Research. His research focuses on theoretical computer science, particularly computational complexity, including derandomization, algebraic complexity, and matrix multiplication algorithms. Education: Ph.D. in Computer Science, UC Berkeley (2000); Postdoc, Microsoft Research (2000-2002). Research interests span computational complexity, explicit constructions, and hardness of approximation. His work often intersects algebraic methods and group theory to advance algorithm design, such as group-theoretic approaches to matrix multiplication. Professional Activities: Program committee member for FOCS 2024, STOC, SODA, and others. Vice-Chair of SIGACT (2021-24). Editor for Theory of Computing (ToC), ACM Transactions on Computation Theory (TOCT), and Computational Complexity (CC). Member of the ECCC scientific board. Research Trends: Recent articles explore fast matrix multiplication via matrix groups, algebraic problems over finite fields, and generalized DFTs for finite groups. His work bridges theoretical foundations with algorithmic innovations in algebraic structures. Grants & Labs: His research is supported by NSF grants focused on algebraic methods in complexity theory. He advises students in theoretical computer science and has contributed to collaborative projects on computational algebra and combinatorics.

Igor ShinkarView profile

Associate Professor

• Computational Complexity
• Property Testing
• Discrete Mathematics
• +3 more

Igor Shinkar is an Associate Professor in the School of Computing Science at Simon Fraser University (SFU). His research focuses on theoretical computer science, discrete mathematics, and probability theory, with particular interests in computational complexity, property testing, and local-to-global phenomena in combinatorial objects. He holds a PhD in Mathematics and Computer Science from the Weizmann Institute of Science (2014), an MSc from the same institution (2009), and a BSc in Mathematics and Computer Science from Tel Aviv University (2005). Shinkar has taught numerous courses at SFU, including CMPT 125 (Introduction to Computing Science and Programming II), CMPT 225 (Data Structures and Programming), CMPT 405/705 (Design and Analysis of Algorithms), and CMPT 706 (Design and Analysis of Algorithms for Big Data). He has also co-taught courses at UC Berkeley and NYU, focusing on topics like coding theory and approximation algorithms. His research explores the interplay between theoretical computer science and mathematics, with contributions to areas such as randomized algorithms, approximation algorithms, and probabilistically checkable proofs. He actively seeks MSc and PhD students interested in theoretical computer science.

Ryan WilliamsView profile

Professor

• Algorithm Design
• Computational Complexity
• Theoretical Computer Science
• +4 more

Ryan Williams is a Professor of Electrical Engineering and Computer Science at MIT's Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Department of Electrical Engineering and Computer Science. Previously, he held a faculty position at Stanford University from 2011 to 2016. He obtained his PhD in Computer Science from Carnegie Mellon University under Manuel Blum and completed his undergraduate studies at Cornell University. His research focuses on computational complexity theory, exploring the boundaries of efficient computation and connections between algorithm design and complexity lower bounds. He teaches advanced courses such as Automata, Computability, and Complexity Theory at MIT. Education: PhD in Computer Science, Carnegie Mellon University (Advisor: Manuel Blum) Bachelor's Degree in Computer Science, Cornell University Research Interests: His work addresses fundamental questions in theoretical computer science, including the P vs. PSPACE problem, circuit lower bounds, and the development of algorithms with provable efficiency. He investigates connections between algorithmic techniques and complexity-theoretic limitations, aiming to establish barriers to solving computational problems efficiently. Publications and Trends: Ryan Williams' recent work spans topics like space-bounded computation, probabilistic polynomial sparsity, circuit lower bounds, and algorithms for compression and graph problems. His research often bridges theoretical insights with practical algorithm design, emphasizing the interplay between computational models and their limitations. Advising and Grants: Current advisees include Rahul Ilango, Ce Jin, and Ted Pyne. He has mentored numerous PhD students who have contributed to areas like fine-grained complexity and circuit analysis. While specific grants are not detailed, his research aligns with foundational studies in theoretical computer science. Labs and Teams: Williams is affiliated with MIT CSAIL, where he collaborates on projects exploring computational complexity and algorithmic foundations.

Sourya RoyView profile

Assistant Professor

• Theoretical Computer Science
• Pseudorandomness
• Coding Theory
• +4 more

Sourya Roy is an Assistant Professor in the Department of Computer Science at the University of Iowa. Prior to his academic career, he worked as a Data Scientist at Foursquare. He earned his PhD in Computer Science from the University of California, Riverside in 2022, advised by Silas Richelson and Amey Bhangale. Education: PhD in Computer Science (University of California, Riverside, 2022) His research spans theoretical and applied domains, focusing on pseudorandomness, coding theory, and cryptography in theoretical computer science, while also contributing to computer vision and machine learning applications. Recent publications emphasize expander graph theory, coding algorithms, and spatio-temporal modeling. Dr. Roy is actively seeking PhD students to collaborate on theoretical computer science projects. His publications highlight expertise in pseudorandomness, expander graphs, and scalable unsupervised learning techniques. Key conferences include FOCS, ECCV, and IEEE Transactions on Information Theory.

Joshua A. GrochowView profile

Associate Professor

• Theoretical Computer Science
• Computational Complexity
• Algebraic Complexity
• +16 more

Joshua A. Grochow is an Associate Professor in the Department of Computer Science and, by courtesy, in the Department of Mathematics at the University of Colorado at Boulder. He is a member of the CS Theory Group and the Complex Systems Group. Previously, he was an Omidyar Fellow at the Santa Fe Institute and a postdoctoral researcher at the University of Toronto. He completed his Ph.D. in Computer Science at the University of Chicago under Lance Fortnow and Ketan Mulmuley. His research bridges theoretical computer science and mathematics, focusing on algebraic complexity, group and tensor isomorphism, proof systems, and the theory of complex systems. His work explores deep connections between algebra, computation, and complexity, with applications in quantum information, ecology, and thermodynamics. He has developed new frameworks for matrix multiplication using infinite groups and advanced the theory of higher-order interactions in complex networks. His recent publications span top venues such as ITCS, CCC, FCT, and SIAM Journal on Computing, with notable results on tensor isomorphism completeness, parallel group isomorphism algorithms, and algebraic proof complexity. He has mentored several Ph.D. students to completion, including Michael Levet, Maya Ornstein, and Tzu-Chi Yen. Best Paper Award at FCT 2023 Outstanding Faculty Mentor Award, CU Boulder (2021–2022) Dean’s Faculty Fellowship (2021) He actively collaborates across disciplines and is involved in educational innovation, including standards-based grading in large algorithms courses. His recent work emphasizes the role of symmetry, structure, and algebra in understanding computational complexity and emergent phenomena.

Noga AlonView profile

Professor

• Combinatorics
• Graph Theory
• Theoretical Computer Science
• +5 more

Noga Alon is the Baumritter Professor Emeritus of Mathematics and Computer Science at Tel Aviv University and currently a Professor of Mathematics at Princeton University. He received his Ph.D. from the Hebrew University of Jerusalem in 1983 and has held visiting positions at MIT, Harvard, Institute for Advanced Study, IBM Almaden, Bell Labs, and Microsoft Research. Research Focus His work centers on combinatorics and graph theory applied to theoretical computer science. Key contributions include: Expander graphs and applications Derandomization techniques Streaming algorithms foundation Algebraic/probabilistic methods in discrete mathematics Information theory and combinatorial geometry Honors and Leadership Member: Israel Academy of Sciences, Academia Europaea Fellow: ACM, AMS Editorial board member for 12+ technical journals Plenary speaker at International Congress of Mathematicians (2002) Supervised 25+ PhD students. Authored 600+ research papers and the book The Probabilistic Method (4th ed., 2016).