جست‌وجوی استاد و پژوهشگر

• Theoretical computer science
• Randomness and Pseudorandomness
• Communication Complexity
• +۳ مورد دیگر

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
• +۵ مورد دیگر

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 Guoمشاهده پروفایل

استادیار

• Replicable data analysis
• Causal inference
• Uncertainty quantification
• +۳ مورد دیگر

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

Chris Umansمشاهده پروفایل

استاد

• Theoretical Computer Science
• Computational Complexity
• Derandomization
• +۴ مورد دیگر

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 Shinkarمشاهده پروفایل

دانشیار

• Computational Complexity
• Property Testing
• Discrete Mathematics
• +۳ مورد دیگر

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 Williamsمشاهده پروفایل

استاد

• Algorithm Design
• Computational Complexity
• Theoretical Computer Science
• +۴ مورد دیگر

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 Royمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Pseudorandomness
• Coding Theory
• +۴ مورد دیگر

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.

Jad Silbakمشاهده پروفایل

پژوهشگر ارشد

• Cryptography
• Privacy
• Complexity Theory
• +۲ مورد دیگر

Jad Silbak is a Postdoctoral Researcher at Northeastern University , affiliated with the Department of Computer Science and funded by the Khoury College Distinguished Postdoctoral Fellowship . Prior to this, he held a postdoctoral position at Tel Aviv University under Benny Applebaum. His research lies at the intersection of Cryptography , Privacy , and Complexity Theory , focusing on: Secure computation protocols and oblivious transfer Differentially private algorithms and communication complexity Error-correcting/detecting codes under computational constraints Non-malleable codes and entropy-based cryptographic constructions Derandomization techniques for bounded models Publications highlight advancements in code design for computationally bounded channels, cryptographic lower bounds, and privacy-preserving protocols. He has presented at top-tier venues including CRYPTO, STOC, and FOCS. Scientific awards include: Khoury College Distinguished Postdoctoral Fellowship He has served as a teaching assistant at Tel Aviv University and the University of Haifa for courses in algorithms and computational models.

• Theoretical Computer Science
• Computational Complexity Theory
• Computational Number Theory
• +۲ مورد دیگر

Manindra Agrawal is a Professor in the Department of Computer Science and Engineering at the Indian Institute of Technology Kanpur (IIT Kanpur), where he has been a faculty member since 1996. His research focuses on theoretical aspects of computer science with particular emphasis on computational complexity theory and computational number theory. Dr. Agrawal completed his B.Tech in Computer Science from IIT Kanpur in 1986 and his PhD in Computer Science from the same institution in 1991. His doctoral thesis, titled "Towards a Characterization of NP-Complete Sets," was supervised by Professor Somenath Biswas. His research interests span computational complexity theory, computational number theory, algebra, and cryptography. Dr. Agrawal is best known for designing the first efficient and deterministic algorithm for testing if a number is prime, a groundbreaking result published in the Annals of Mathematics in 2004 as "PRIMES is in P." This work has had a profound impact on theoretical computer science and number theory, resolving a long-standing open problem in the field. His publication record shows a consistent focus on fundamental problems in theoretical computer science, particularly in complexity theory and number-theoretic algorithms. His work ranges from theoretical foundations like the isomorphism conjecture for constant depth reductions to practical applications such as designing encryption algorithms for the Indian Navy and Air Force. Padma Shri (2013) Infosys Prize (2008) Godel Prize (2006) Shanti Swarup Bhatnagar Award (2003) Clay Research Award (2002) Fellow of the Indian National Science Academy (FNA) Fellow of The World Academy of Sciences (FTWAS) Fellow of the Indian Academy of Engineering (FNAE) Fellow of the Indian Academy of Sciences (FASc) Dr. Agrawal has advised several notable students, including Nitin Saxena and Satyadev Nandkumar, who have made significant contributions to theoretical computer science. His research group has been instrumental in advancing knowledge in computational complexity and number theory. His work on primality testing has not only theoretical significance but has also influenced practical cryptographic applications.