جست‌وجوی استاد و پژوهشگر

Lijie Chenمشاهده پروفایل

استادیار

• Theoretical Computer Science
• Computational Complexity Theory
• Quantum Complexity Theory
• +۴ مورد دیگر

Lijie Chen is an Assistant Professor in the Department of Electrical Engineering and Computer Sciences at UC Berkeley, where he is part of the Berkeley Theory Group. Previously, he was a Miller Research Fellow at UC Berkeley, hosted by Avishay Tal and Umesh Vazirani, and earned his Ph.D. from MIT under Ryan Williams. His research focuses on theoretical computer science, particularly computational complexity theory, with applications to quantum physics and AI safety. Education: Ph.D. in Computer Science from MIT (2022), B.Sc. from Yao Class at Tsinghua University. Research Interests: Complexity theory, quantum complexity, derandomization, circuit lower bounds, and foundational aspects of AI safety. Chen has made significant contributions to understanding fundamental questions in complexity theory, including circuit lower bounds and the connections between randomness and computation efficiency. His work often bridges theoretical insights with practical implications in quantum computing and algorithm design. Awards and Honors: Machtey Award for Best Student Paper (2019). Danny Lewin Best Student Paper Award (2019). Invited to SICOMP Special Issues for FOCS and STOC papers. He has organized workshops on complexity theory and derandomization, and his research has been recognized in venues like STOC, FOCS, and the Journal of the ACM.

Mohsen Ghaffariمشاهده پروفایل

دانشیار

• Theoretical Computer Science
• Distributed Algorithms
• Parallel Algorithms
• +۳ مورد دیگر

Mohsen Ghaffari is an Associate Professor at MIT's Department of Electrical Engineering and Computer Science (EECS), holding the Steven and Renee Finn Chair. His research focuses on theoretical computer science, particularly distributed and parallel algorithms, graph theory, and network optimization. Formerly, he was a tenured CS faculty member at ETH Zurich until 2022. PhD in Computer Science from MIT (2016) His research interests include distributed algorithms, parallel computing, graph decomposition, and network congestion management. His recent work addresses coreness decomposition, spanner construction, and Euclidean k-center optimization in massive parallel computation frameworks. Notable scientific awards include the ACM Doctoral Dissertation Award (Honorable Mention), ACM-EATCS Doctoral Dissertation Award, and multiple best paper awards at FOCS, PODC, and SODA. He has advised numerous PhD and Master's students, many of whom have transitioned to academic and industry roles. He has taught courses at MIT and ETH Zurich on distributed algorithms, advanced algorithms, and massively parallel computation. His professional activities include serving on program committees for SODA, FOCS, STOC, and organizing workshops like Highlights of Algorithms (HALG) and Workshop on Local Algorithms (WOLA).

• Finite Model Theory
• Graph Structure Theory
• Computational Complexity
• +۳ مورد دیگر

Kord Eickmeyer is a Lecturer at Technische Universität Darmstadt in the Department of Mathematics, specializing in the mathematical logic group. He holds a PhD in mathematics from Humboldt University Berlin and has held postdoctoral positions at TU Darmstadt (2011–2017) and the National Institute of Informatics in Tokyo (2011–2013). His research focuses on finite model theory, graph structure theory, and computational complexity, particularly in descriptive and parameterized complexity, as well as randomization and derandomization techniques. Research interests include exploring the boundaries of computational complexity through logical frameworks, analyzing graph structures for efficient algorithm design, and investigating the role of randomness in computation. His work bridges theoretical computer science and mathematical logic, with applications in algorithm design and formal methods. Publications span topics from model-checking on ordered structures to gap-planar graphs and randomized logics. Collaborations include prominent institutions like the National Institute of Informatics and Humboldt University Berlin. No scientific awards are explicitly listed, but his extensive academic contributions reflect a strong research trajectory. Advising and grants are not detailed in the provided text, though his academic career includes supervision roles during his PhD and postdoctoral phases. His involvement with the mathematical logic group at TU Darmstadt highlights collaborative research efforts in foundational areas of computer science and mathematics.

Nikita Zhivotovskiyمشاهده پروفایل

استادیار

• Mathematical Statistics
• Applied Probability
• Statistical Learning Theory
• +۵ مورد دیگر

Nikita Zhivotovskiy is an Assistant Professor in the Department of Statistics at the University of California Berkeley within the College of Letters and Science. His research spans the intersection of mathematical statistics, probability theory, and statistical learning theory with particular focus on high-dimensional data analysis and non-parametric inference. His research interests include mathematical statistics, applied probability, statistical learning theory, high-dimensional data analysis, non-parametric inference, and artificial intelligence/machine learning. Zhivotovskiy's work addresses fundamental questions in statistical learning theory, including risk bounds, algorithmic stability, and convergence rates, with applications spanning multiple domains including robust statistics and private learning. His recent publications (2021-2025) demonstrate significant contributions to theoretical machine learning, particularly in statistical learning theory, risk bounds, high-dimensional statistics, and algorithmic stability. These works appear in top venues including NeurIPS, COLT, and FOCS, reflecting the theoretical depth and importance of his contributions to the field. Among his notable achievements is a Best Paper Award at the Conference on Learning Theory (COLT) in 2020 for his work on 'Proper Learning, Helly Number, and an Optimal SVM Bound.' Zhivotovskiy has also contributed to the theoretical foundations of PAC learning, risk minimization, and statistical aggregation. Prior to his current position, Zhivotovskiy was a postdoctoral researcher at ETH Zürich (2021-2022) and Google Research (2019-2020). He completed his PhD at Moscow Institute of Physics and Technology in 2018 under the supervision of Vladimir Spokoiny and Konstantin Vorontsov.

James Aspnesمشاهده پروفایل

استاد

• Distributed Algorithms
• Computer Science
• Randomized Algorithms
• +۵ مورد دیگر

James Aspnes is the Harold W. Cheel Professor of Computer Science at Yale University, specializing in distributed algorithms and randomized methods. He holds a PhD from Carnegie Mellon University and degrees from MIT. His research focuses on distributed systems, peer-to-peer networks, and sensor networks, emphasizing tools for efficient data management and fault-tolerance. Education: PhD (CMU, 1992), SM & SB (MIT, 1987) Affiliations: Yale since 1993, IBM Almaden Research Center (1992–1993) Research interests include distributed algorithms, randomization, and applications in biology and economics. Notable contributions include skip graphs, population protocols, and consensus algorithms. He has received the ACM-EATCS Dijkstra Prize (2020) and Dylan Hixon Prize (2000). Publications span distributed computing, algorithms, and cryptography. Recent work explores consensus protocols and privacy in population models. Grants include NSF awards totaling over $2M. Active in editorial roles (Algorithmica, Distributed Computing) and conference organization (PODC 2005, DCOSS 2007).

Christopher Umansمشاهده پروفایل

استاد

• Computational Complexity
• Theoretical Computer Science
• Algebraic Algorithms
• +۵ مورد دیگر

Christopher Umans is a Professor of Computer Science at the California Institute of Technology, where he serves as the William M. Coughran Jr. Leadership Chair and Executive Officer for the Department of Computing and Mathematical Sciences. He joined Caltech in 2002 as an Assistant Professor, became Associate Professor in 2008, and was promoted to Professor in 2010. He has held multiple leadership positions including Division Deputy Chair (2018-2020) and Executive Officer since 2020. His educational background includes a B.A. from Williams College (1996) and a Ph.D. in Computer Science from the University of California, Berkeley (2000), where he was advised by Christos Papadimitriou. After completing his Ph.D., he was a postdoc at Microsoft Research from 2000-2002 before joining Caltech. Professor Umans's research focuses on theoretical computer science, particularly computational complexity with an algebraic flavor. His work spans derandomization, explicit combinatorial constructions, algebraic algorithms, coding theory, and hardness of approximation. He has made significant contributions to understanding the complexity of fundamental problems like matrix multiplication through group-theoretic approaches and developing fast algorithms for generalized discrete Fourier transforms over finite groups. His recent publications reveal a strong emphasis on algebraic methods in computation, with particular focus on matrix multiplication algorithms, generalized DFTs, and polynomial factorization. His work consistently bridges theoretical computer science with deep mathematical concepts from group theory, representation theory, and algebraic combinatorics, demonstrating how these mathematical structures can yield more efficient computational methods. His scientific recognition includes the prestigious Simons Investigator in Computer Science award and the Northrop Grumman Prize for Excellence in Teaching at Caltech. Professor Umans has advised students including Chloe Ching-Yun Hsu, who received the Henry Ford II Scholar Award. His research has been supported by multiple NSF grants including 'AF: Small: Group Theory and Representation Theory in Matrix Multiplication and Generalized DFTs' and 'AF: Small: Algorithms for Matrix Multiplication, Polynomial Factorization and Generalized Fourier Transform.' He serves on numerous program committees including FOCS, STOC, and CCC, and is Vice-Chair of SIGACT (2021-24). He is an active member of Caltech's Theory Group within the Computing and Mathematical Sciences department, contributing to its research direction and mentoring junior researchers. His work often involves collaborations with researchers across institutions, as evidenced by his extensive publication record with co-authors from various universities.

Rocco Servedioمشاهده پروفایل

استاد

• Computational Complexity Theory
• Computational Learning Theory
• Property Testing
• +۳ مورد دیگر

Rocco Servedio is a Professor in the Department of Computer Science at Columbia University, where he leads research in theoretical computer science with a focus on computational complexity theory, learning theory, and the role of randomness in computation. He previously served as Chair of the Computer Science Department from 2018 to 2021. He holds a Ph.D., MS, and AB in Mathematics from Harvard University. His research interests include property testing, computational learning theory, and algorithmic lower bounds. He has contributed to foundational work in areas like junta testing, trace reconstruction, and convexity testing. Servedio has held leadership roles in major conferences such as STOC, CCC, and COLT, and has mentored students through courses like Unconditional Lower Bounds and Derandomization . Education: Ph.D. in Computer Science, Harvard University MS in Computer Science, Harvard University AB in Mathematics, Harvard University His research bridges theoretical computer science and applied mathematics, with recent work exploring the intersection of Gaussian processes, convex geometry, and algorithmic efficiency. Servedio's contributions to the field are exemplified through his involvement in high-impact conferences and his leadership in advancing fundamental computational theories.

Ran Razمشاهده پروفایل

استاد

• Computational Complexity Theory
• Boolean Circuit Complexity
• Communication Complexity
• +۲ مورد دیگر

Ran Raz is a Professor of Theoretical Computer Science at Princeton University. He earned his B.Sc. in Mathematics and Physics (1987) and Ph.D. in Mathematics (1992) from the Hebrew University of Jerusalem. After postdoctoral work at Princeton (1994), he joined the Weizmann Institute of Science in Israel and held visiting positions at Microsoft Research (2006, 2009) and the Institute for Advanced Study (2012–2016). Education: B.Sc. in Mathematics and Physics, Hebrew University of Jerusalem (1987) Ph.D. in Mathematics, Hebrew University of Jerusalem (1992) Research Interests: Computational Complexity Theory Boolean and Arithmetic Circuit Complexity Probabilistically Checkable Proofs Quantum Computation and Communication Randomness and Derandomization

Ran Razمشاهده پروفایل

استاد

• Complexity Theory
• Quantum Computation
• Circuit Complexity
• +۲ مورد دیگر

Professor Ran Raz specializes in complexity theory at Princeton University's Department of Computer Science. His research targets lower bounds in computational models, quantum communication, and derandomization. Current investigations include quantum logspace verifiability and memory-sample tradeoffs in learning algorithms. Research pillars: Boolean/arithmetic circuit complexity Quantum computation and communication Probabilistically checkable proofs Publications reflect a focus on quantum-classical hybrid systems and foundational limits of computation. Lab affiliations: Quantum research group at Princeton CS.

• Cryptography
• Theoretical Computer Science
• Zero-Knowledge Proofs
• +۵ مورد دیگر

Nir Bitansky is an associate professor of computer science at New York University's Courant Institute, part of the Theory Group and Cryptography Group. He previously held a faculty position at Tel Aviv University (now on leave) and was a postdoctoral researcher at MIT CSAIL, advised by Vinod Vaikuntanathan. His Ph.D. from Tel Aviv University was under Ran Canetti. His research focuses on foundational aspects of cryptography and computational complexity, including zero-knowledge proofs, obfuscation, and post-quantum security. Research Interests: His work bridges theoretical computer science and cryptography, addressing topics such as secure computation, cryptographic hardness assumptions (e.g., LWE, PPAD), privacy-preserving protocols, and quantum-resistant cryptography. Key areas include randomized encodings, succinct arguments, and the interplay between complexity theory and cryptographic primitives. Publications: His recent work emphasizes post-quantum cryptography (e.g., zero-knowledge protocols, commitment schemes) and foundational results in obfuscation. Notable contributions include advancements in batch proofs, non-interactive arguments, and the cryptographic hardness of local search problems. Students and Postdocs: Advised Ph.D. students include Sapir Freizeit, Nathan Geier, and Omri Shmueli (co-advised with Zvika Brakerski). Hosted postdocs such as James Bartusek (now at Columbia) and Rachit Garg. Collaborates widely, co-authoring papers with figures like Vinod Vaikuntanathan and Omer Paneth. Affiliations: Serves on program committees for top venues (e.g., CRYPTO, EUROCRYPT, FOCS). Taught courses on cryptography fundamentals, advanced topics, and lattices at TAU and NYU.

Kasper Green Larsenمشاهده پروفایل

استاد

• Machine Learning
• Theoretical Computer Science
• Algorithms and Data Structures
• +۳ مورد دیگر

Kasper Green Larsen is a Professor in the Department of Computer Science at Aarhus University. His research focuses on theoretical computer science, machine learning, algorithms, and data structures. He has made significant contributions to boosting algorithms, PAC learning theory, and computational geometry. His work often bridges algorithm design with complexity theory, addressing challenges in optimization, memory efficiency, and lower bounds analysis. Key research areas include: Algorithmic Learning Theory (e.g., boosting, bagging, and PAC learners) Data Structure Design (e.g., invertible Bloom tables, succinct representations) Computational Complexity (e.g., lower bounds for dynamic and oblivious algorithms) Geometric Algorithms (e.g., hierarchical searching, range queries) Recent publications emphasize foundational advancements in learning theory (e.g., optimal weak-to-strong learning) and data efficiency (e.g., memory-reduced Bloom filters). His work frequently appears in top conferences like IJCAI, ICALP, and SODA, reflecting rigorous theoretical contributions with practical implications.

Roei Tellمشاهده پروفایل

استادیار

• Complexity Theory
• Computational Complexity
• Pseudorandomness
• +۵ مورد دیگر

Roei Tell is an Assistant Professor at the University of Toronto, affiliated with the Theory Group in the Department of Computer Science and the Department of Computer and Mathematical Sciences, Scarborough. His research focuses on computational complexity theory, particularly the role of randomness in computation, pseudorandomness, and derandomization. He holds a PhD from the Weizmann Institute of Science, advised by Oded Goldreich, and completed postdoctoral work at MIT under Ryan Williams, as well as fellowships at the Institute for Advanced Study and the Simons Institute. His work intersects areas like circuit complexity, cryptography, and error-correcting codes. Education: BA (Tel-Aviv University/Open University), MSc and PhD (Weizmann Institute of Science). Research interests include foundational questions in complexity theory, such as P vs NP, and practical implications for algorithm design. His contributions span derandomization techniques, lower bounds, and cryptographic primitives. He collaborates widely, with notable works on succinct non-interactive arguments and zero-knowledge proofs.

• Distributed Computing
• Parallel Algorithms
• Graph Theory
• +۳ مورد دیگر

Christoph Grunau is a Researcher at ETH Zürich's Theoretical Computer Science department, affiliated with the Professorship for Computer Science. His work focuses on distributed computing, parallel algorithms, graph theory, and network decomposition. He has contributed to advancements in scalable MPC (Massively Parallel Computing) algorithms, efficient parallel derandomization techniques, and deterministic network decomposition methods. His research emphasizes algorithmic efficiency, theoretical guarantees, and applications in distributed systems, quantum computing, and dynamic graph problems. Key contributions include work on graph orientation, dynamic coloring algorithms, and clustering techniques such as k-center and k-means++. His publications span topics like shortest path algorithms with negative edge weights, probabilistic methods for algorithm analysis, and distributed symmetry breaking in sparse graphs. Grunau's research bridges foundational theory with practical distributed computing challenges, addressing scalability and efficiency in both classical and emerging computational frameworks.

V. Arvindمشاهده پروفایل

استاد

• Computational Complexity Theory
• Structural Complexity
• Randomized Computation
• +۵ مورد دیگر

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.

• Nonblocking Algorithms
• Concurrent Data Structures
• Memory Reclamation
• +۳ مورد دیگر

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