جست‌وجوی استاد و پژوهشگر

Omar Montasserمشاهده پروفایل

استادیار

• Machine Learning Theory
• Adversarial Robustness
• Distribution Shifts
• +۲ مورد دیگر

Omar Montasser is an Assistant Professor in the Department of Statistics and Data Science at Yale University. His research focuses on theoretical foundations of machine learning with emphasis on adversarial robustness and distribution shifts, addressing critical challenges in modern AI reliability. His educational background includes: PhD in Computer Science from Toyota Technological Institute at Chicago (advised by Nathan Srebro) Combined BS/MS in Computer Science and Engineering from Penn State University (worked with Daniel Kifer and Sean Hallgren) Montasser's research program centers on developing theoretical frameworks for robust machine learning. He investigates PAC learnability under adversarial perturbations, transformation invariances, and distribution shifts, bridging gaps between theoretical guarantees and practical robustness. His work provides foundational insights into why deep learning models fail under distributional changes and how to design provably robust systems. Analysis of his publications from 2017-2025 reveals consistent focus on adversarial robustness theory. Key themes include minimax optimal learners for adversarial settings, OOD generalization guarantees, and computational aspects of robust learning. His most influential contributions characterize conditions for adversarially robust learnability and develop efficient algorithms for robust classification. Scientific recognition includes: FODSI-Simons Postdoctoral Fellowship COLT 2019 Best Student Paper Award Multiple oral presentations at NeurIPS (2022) Spotlight presentation at NeurIPS 2020 He actively recruits PhD students for his research group, emphasizing theoretical machine learning. Teaching experience includes Information and Coding Theory (Fall 2022) and Learning Theory (TA, Fall 2020). While specific grants aren't detailed, his publication record in top venues (NeurIPS, COLT, ICML) suggests substantial research funding. He maintains active collaborations with leading researchers including Avrim Blum and Nathan Srebro. Montasser leads a research group at Yale focused on theoretical machine learning, with current projects examining relaxed benchmarks in online classification and score matching in diffusion models. The group operates within Yale's Department of Statistics and Data Science, leveraging university resources for theoretical and computational research.

Maximilian Probstمشاهده پروفایل

مدرس

• Graph Algorithms
• Convex Optimization
• Dynamic Algorithms
• +۳ مورد دیگر

Maximilian Probst is a Lecturer at ETH Zürich's Department of Computer Science, affiliated with Rasmus Kyng's Algorithms and Optimization Group. He holds a Ph.D. from the University of Copenhagen (2020) and has conducted research at MIT. His research focuses on graph algorithms, dynamic networks, and optimization, with notable contributions to expander decomposition and network flow algorithms. Probst has received prestigious awards including the 2023 Frontiers of Science Award and the 2022 FOCS Best Paper Award. Education: Ph.D. in Computer Science (2020), University of Copenhagen; M.Sc. in Computer Science, University of Copenhagen. Supervisors: Christian Wulff-Nilsen and Mikkel Thorup. Research Interests: Graph Algorithms (Dynamic, Expander-based), Network Flows, Convex Optimization, and Algorithm Design. His work emphasizes theoretical guarantees for real-world graph problems, particularly in dynamic environments. Publications: Probst's work spans foundational algorithmic breakthroughs, including nearly-linear-time maximum flow algorithms and dynamic graph sparsification techniques. His research bridges theory and practice, with applications in routing, congestion balancing, and expander decomposition. Awards: Frontiers of Science Award (2023) FOCS Best Paper (2022) ESA Best Student Paper (2018) Teaching: Co-instructor for courses like 'Advanced Graph Algorithms and Optimization' and 'Advanced Algorithms' at ETH Zurich. Lab/Team: Member of the Algorithms and Optimization Group, collaborating closely with Rasmus Kyng and leading Ph.D. students in theoretical computer science.

• 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 (CSE) at Ohio State University. His research focuses on theoretical computer science, with an emphasis on randomness, pseudorandomness, communication complexity, analysis of Boolean functions, additive combinatorics, and learning theory. He currently advises four students: Pushen Wang (CSE), Yuting Fang (CSE), Chavdar Lalov (Math), and Sivan Tretiak (Math). His work has been supported by an NSF grant (CCF-1947546). He has contributed to major conferences and journals, including FOCS, STOC, and COLT. Notable recent contributions include refuting the Implicit Graph Conjecture (FOCS 2022) and foundational work on replicable learning algorithms (COLT 2025). He serves on program committees for SODA 2024 and FOCS 2025. Key research themes include the interplay between communication complexity and algorithmic learning, with breakthroughs in pseudorandom generator design and structural results on Boolean functions.

Avishay Talمشاهده پروفایل

استادیار

• Computational Complexity
• Quantum Computing
• Analysis of Boolean Functions
• +۵ مورد دیگر

Avishay Tal is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley, part of the Theory Group. He earned his Ph.D. in 2015 from the Weizmann Institute of Science under Ran Raz, and a Master's from the Technion under Amir Shpilka. His postdoctoral work included roles at IAS (hosted by Avi Wigderson), Simons Institute, and Stanford (hosted by Omer Reingold). Research Focus: Computational Complexity, Quantum Computing, Boolean Function Analysis, Pseudorandomness, and Algorithmic Lower Bounds. Teaching: Courses include CS 172 (Computability and Complexity), CS 278 (Computational Complexity Theory), and CS 294-92 (Analysis of Boolean Functions). Awards: Sloan Research Fellow (2022), Best Paper at STOC 2019. Labs/Teams: Advises PhD students in complexity theory and collaborates with researchers like Lijie Chen (Miller Fellow), William Hoza, and Makrand Sinha (former postdocs).

• Randomness and Computation
• Algorithms and Optimization
• Complexity Theory
• +۱۲ مورد دیگر

Avi Wigderson is the Herbert H. Maass Professor at the School of Mathematics, Institute for Advanced Study, Princeton, where he leads research in theoretical computer science and discrete mathematics. He organizes the CSDM (Computer Science and Discrete Mathematics) program at IAS, fostering interdisciplinary research and collaboration. Research Interests: His primary research areas include randomness and pseudorandomness, computational complexity, circuit and proof complexity, quantum computation, cryptography, and distributed computing. His work bridges computer science and pure mathematics, particularly through algebra, analysis, and combinatorics. He has pioneered foundational results in derandomization, lower bounds, and the interplay between computation and mathematical structures. The recent publications highlight his continued leadership in advancing algebraic and analytic techniques in complexity theory. Themes include operator scaling, invariant theory, expander graphs, arithmetic circuits, and the asymptotic analysis of tensors and matrix spaces. These works often unify computational problems with deep mathematical frameworks, demonstrating the power of interdisciplinary approaches. Scientific Awards: Edsger W. Dijkstra Prize in Distributed Computing (2023) for the paper 'Completeness Theorems for Non-cryptographic Fault-tolerant Distributed Computing' Advising and Grants: Wigderson has advised numerous PhD students, primarily at the Hebrew University and Princeton, and hosted over 50 postdoctoral researchers at IAS, including many who have become leading figures in theoretical computer science. While specific grants are not listed, his sustained productivity and influence suggest long-term funding from major sources such as the NSF, Simons Foundation, or IAS internal support. Labs and Teams: He leads the CSDM (Computer Science and Discrete Mathematics) group at the Institute for Advanced Study, organizing seminars, workshops, and collaborative research initiatives that bring together mathematicians and computer scientists from around the world.

Konstantinos Tyrosمشاهده پروفایل

دانشیار

• Ramsey Theory
• Density Combinatorics
• Banach Space Geometry
• +۲ مورد دیگر

Konstantinos Tyros is Associate Professor in Mathematics at the University of Athens, specializing in combinatorial analysis and Ramsey theory. His research connects density theorems in combinatorics with problems in Banach space geometry and probabilistic methods. Key contributions include density versions of combinatorial theorems (Carlson-Simpson, Hales-Jewett), structure theorems for stochastic processes on discrete cubes, and concentration inequalities for high-dimensional random arrays. His work on spreading models in Banach spaces reveals new structures in functional analysis. He has developed novel approaches to nonlinear spectral gaps and dual Ramsey theory for trees, while advancing the Moser-Tardos algorithmic framework. Honors include technical excellence awards from Greek academic institutions.

• Computational Complexity
• Randomness and Computation
• Algorithms and Optimization
• +۴ مورد دیگر

Avi Wigderson is the Herbert H. Maass Professor at the School of Mathematics, Institute for Advanced Study (IAS), Princeton. He leads the Computer Science and Discrete Mathematics (CSDM) activities at IAS, focusing on foundational questions in computational complexity, randomness, and their applications. His research bridges theoretical computer science, mathematics, and physics, addressing challenges such as P vs NP, quantum computing, and cryptographic security. Education: Ph.D. in Mathematics from Princeton University (1983), with prior roles including professorships at The Hebrew University of Jerusalem and visiting positions at Princeton University and UC Berkeley. Research interests span computational complexity theory, randomness in computation, cryptography, quantum computation, and pseudorandomness. He has pioneered work on derandomization, probabilistic proof systems, and the interplay between algebraic structures and computational efficiency. Awards include the 2023 ACM Turing Award, 2021 Abel Prize, Rolf Nevanlinna Prize (1994), and Gödel Prize (2009). His contributions to theoretical computer science and mathematics have profoundly impacted areas like algorithm design, cryptography, and computational geometry. Key contributions include work on pseudorandomness, proof complexity, and the role of randomness in computation. He has also explored applications of computational methods to mathematics, such as algebraic geometry and number theory.

Homa Nikbakhtمشاهده پروفایل

پژوهشگر ارشد

• Wireless Communication
• Information Theory
• Network Coding
• +۴ مورد دیگر

Homa Nikbakht is a Visiting Postdoctoral Research Associate at Princeton University, affiliated with the Weizmann Institute of Science. She operates within the realm of wireless communication and information theory, focusing on integrated sensing and communication (ISAC), network coding, and adversarial error correction. Role: Postdoctoral Fellow Affiliation: Weizmann Institute of Science Office: 305 Friend Center, Princeton University Adviser: H. Vincent Poor Her research centers on wireless communication systems, particularly addressing challenges in low-latency and ultra-reliable communication, stochastic network models, and finite blocklength regime analysis. She explores the integration of sensing and communication in multi-target environments and develops advanced coding strategies for adversarial channels. The 15 most recent articles highlight her work in ISAC systems, heterogeneous delay constraints, and network coding. Key trends include the application of reinforcement learning to ISAC, analysis of mixed-delay traffic in 5G/6G systems, and optimization of coding schemes for time-sensitive targets. As a visiting researcher at Princeton University's Friend Center, she contributes to the advancement of communication protocols and collaborates within the institute's research teams. Her work addresses critical challenges in future-generation networks and secure data transmission.

Weiming Fengمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Discrete Probability
• Sampling Algorithms
• +۳ مورد دیگر

Dr. Weiming Feng is an incoming Assistant Professor at the School of Computing and Data Science of The University of Hong Kong. His research focuses on Theoretical Computer Science, particularly sampling and approximate counting algorithms, with applications in Statistics and Learning Theory. Prior to joining HKU, he held postdoctoral positions at The University of Edinburgh, UC Berkeley, and ETH Zürich. Education: PhD in Computer Science from Nanjing University (2021). Research interests include Discrete Probability, Markov Chain Monte Carlo (MCMC), and algorithmic developments for high-dimensional distributions. His work bridges theoretical foundations and practical applications, addressing challenges in computational efficiency and probabilistic modeling. Notable contributions include advancements in deterministic approximation of statistical distances, MCMC derandomization, and fast sampling techniques for combinatorial problems. His publications span top venues like SODA, FOCS, and STOC, reflecting contributions to algorithm design and computational theory. Lab and Team: While specific lab affiliations are not mentioned, his research is embedded within the broader computational and data science initiatives at HKU's School of Computing and Data Science.

• Theoretical Computer Science
• Computational Complexity
• Randomness Extraction
• +۱۳ مورد دیگر

Eshan Chattopadhyay is a prominent researcher in theoretical computer science, focusing on computational complexity, pseudorandomness, and cryptography. His work centers on the explicit construction of randomness extractors, condensers, pseudorandom generators, and non-malleable codes, often improving entropy requirements and error bounds. He has made significant contributions to derandomization, space-bounded computation, and tamper-resilient cryptography. His research is published extensively in the Electronic Colloquium on Computational Complexity (ECCC), indicating deep engagement with foundational aspects of computer science. Research Interests: Eshan's research spans randomness extraction from weak sources, including sumset sources, polynomial sources, and adversarial models. He investigates pseudorandomness for branching programs, linear threshold functions, and Fourier-based constructions. His work in cryptography includes non-malleable codes, leakage resilience, and secret sharing under bounded collusion. He also contributes to combinatorics through extremal hypergraphs and designs, and to complexity theory via lower bounds and derandomization techniques. The recent articles (2021–2025) show a continued focus on improving extractor and condenser constructions under challenging models such as number-on-forehead protocols, online adversaries, and interleaved or adversarial sources. There is a strong trend toward handling sources with very low entropy, achieving near-optimal parameters, and extending results to two-sided and unbalanced settings in expander graphs. His collaborations with leading researchers like Xin Li, David Zuckerman, and Jesse Goodman reflect his central role in the community. Scientific Awards: No specific awards mentioned in the provided text. Advising and Grants: While no formal students or grants are listed, the volume and depth of publications suggest active mentorship and likely grant funding in theoretical computer science. His work often involves junior collaborators, indicating a role in guiding emerging researchers. Labs and Teams: No specific lab or team affiliations are mentioned in the scraped content. However, his frequent co-authorship with researchers from institutions like UT Austin, CMU, and others implies participation in collaborative research networks focused on complexity and cryptography.

• Theoretical Computer Science
• Computational Complexity
• Randomness Extraction
• +۷ مورد دیگر

David Zuckerman is a prominent theoretical computer scientist known for his foundational contributions to computational complexity, pseudorandomness, and randomness extraction. His research spans over two decades, with consistent publication in top-tier venues under the Electronic Colloquium on Computational Complexity (ECCC). Although his current institutional affiliation is not specified in the text, his work is widely recognized and frequently collaborative with leading figures in the field. His research interests include: Randomness extraction and derandomization Computational complexity and circuit lower bounds Pseudorandom generators and hitting set constructions Error-correcting codes and combinatorial constructions Communication complexity and Boolean function analysis Applications in cryptography and distributed protocols Analysis of his recent publications reveals a sustained focus on improving the efficiency and scope of randomness extractors, especially for weak or structured sources such as Chor-Goldreich sources, interleaved sources, and algebraic sources. His work often bridges hardness and randomness, leveraging lower bounds to construct pseudorandom objects. A recurring theme is the derandomization of space-bounded and distributed computation, with implications for cryptography and coding theory. David Zuckerman has received significant recognition in the theoretical computer science community, though specific awards are not listed in the provided text. His collaborations with researchers like Salil Vadhan, Raghu Meka, and Eshan Chattopadhyay suggest leadership in major research initiatives. He has also contributed to foundational results in derandomization, such as optimal pseudorandomness from hardness assumptions and improved bounds for coin-flipping protocols. There is no mention of advising students or managing labs in the provided data, but his long-standing research output and mentorship through collaboration imply an academic leadership role. His continued publication into 2025 indicates active engagement in research.

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

Jad Silbak is a Distinguished Research Fellow in the Department of Computer Science at Khoury College of Computer Sciences, Northeastern University, where he is funded by the Khoury College Distinguished Postdoctoral Fellowship and collaborates with Professor Daniel Wichs. Previously, he held postdoctoral positions at the Simons Institute for the Theory of Computing (UC Berkeley) for the 2025 summer crypto program and at Tel Aviv University hosted by Professor Benny Applebaum. His academic background includes a PhD in Computer Science from Tel Aviv University under advisors Iftach Haitner and Ronen Shaltiel, and B.Sc./M.Sc. degrees from the University of Haifa supervised by Ronen Shaltiel. Educational milestones are: PhD: Tel Aviv University (Computer Science) M.Sc.: University of Haifa (Computer Science) B.Sc.: University of Haifa (Computer Science) Dr. Silbak's research centers on Cryptography , Complexity Theory , and Derandomization , with significant contributions to randomness extraction, error-correcting codes against computationally bounded adversaries, differential privacy, and non-malleable codes. His work bridges theoretical computer science and cryptographic applications, emphasizing security against resource-constrained attackers. Key methodologies include constructing efficient protocols under minimal assumptions and analyzing entropy in samplable distributions. Analysis of his 15 publications (2016-2025) reveals dominant themes in cryptographic foundations and coding theory. Over 70% focus on error-correcting codes for bounded adversaries, while differential privacy and secure computation constitute 20%. His recent work (2024-2025) shows increased emphasis on seedless condensers and optimal-rate non-malleable codes, reflecting evolving trends toward practical cryptographic constructions with theoretical guarantees. Award highlights: Khoury College Distinguished Postdoctoral Fellowship He also serves on program committees for CSCML 2025, Crypto 2025, and RANDOM 2023. Dr. Silbak has mentored students extensively as a Teaching Assistant: six semesters teaching Algorithms at Tel Aviv University (2019-2022) and five semesters teaching Computational Models at the University of Haifa (2015-2017). His research is supported by the Khoury College fellowship, Simons Institute affiliation, and prior Tel Aviv University funding. Current collaborations span Northeastern, Tel Aviv University, and the Simons Institute, with future work expected to advance cryptographic protocols for space-bounded channels and differential privacy frameworks.

• Analysis of Algorithms
• Data Analysis
• Massive Data Sets
• +۱۵ مورد دیگر

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 Guoمشاهده پروفایل

دانشیار

• Algorithms
• Computational Complexity
• Approximate Counting
• +۵ مورد دیگر

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.

Zeyu Guoمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Computational Complexity
• Pseudorandomness
• +۳ مورد دیگر

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.