جست‌وجوی استاد و پژوهشگر

Alistair Sinclairمشاهده پروفایل

استاد

• Randomized Algorithms
• Markov Chain Monte Carlo
• Statistical Physics
• +۳ مورد دیگر

Alistair Sinclair is the Kikuo Ogawa and Kaoru Ogawa Professor of Computer Science in the Department of Electrical Engineering and Computer Sciences at UC Berkeley. He received his BA in Mathematics from the University of Cambridge (1982) and PhD in Computer Science from the University of Edinburgh (1988). After briefly serving on faculty at Edinburgh, he joined UC Berkeley in 1994. Sinclair has held visiting positions at DIMACS, Princeton University, Rutgers University, Microsoft Research, École Polytechnique, University of Paris-Orsay, and University of Rome III. His research explores: Randomized algorithms and Markov chain Monte Carlo methods Phase transitions in statistical physics Algorithmic applications of stochastic processes Nonlinear dynamical systems Combinatorial optimization Analysis of Sinclair's recent publications (2017-2025) reveals strong emphasis on statistical physics models (especially Ising and random-cluster systems), Markov chain dynamics, phase transitions, and algorithmic solutions for combinatorial problems. Key methodologies include spatial mixing analysis, entropy decay measurements, and deterministic approximation techniques. Scientific Awards: 1996 ACM-EATCS Gödel Prize 2006 Fulkerson Prize 2017 SIGACT Distinguished Service Prize Sinclair has advised 17+ PhD students including notable researchers in theoretical computer science and mathematics. He served as Founding Associate Director (2012-2017) of the Simons Institute for the Theory of Computing, receiving recognition for developing its research programs on probability, geometry, and computational complexity.

François Pirotمشاهده پروفایل

دانشیار

• Graph Theory
• Combinatorics
• Probabilistic Method
• +۴ مورد دیگر

François Pirot is an Associate Professor (Maître de Conférences) at Université Paris-Saclay since September 1, 2021. He conducts research at the LISN laboratory within the GALaC team and teaches at the Faculty of Science of Orsay. PhD in Mathematics (Radboud University) and Computer Sciences (Université de Lorraine), 2019 Postdoctoral experience: ULB (2019), G-SCOP (2019-2020), Inria Sophia Antipolis (2020-2021) His research focuses on graph coloring problems in diverse contexts such as graph powers, locally sparse graphs, and distributed algorithms, utilizing probabilistic methods and connections to bio-informatics through circular codes. He has advanced bounds for h -conflict-free coloring, acyclic coloring, and dichromatic numbers in oriented graphs, with applications to minor-closed families and geometric group theory. Scientific contributions include: Asymptotically tight bounds for chromatic numbers in sparse graphs Efficient fractional coloring algorithms for K_t-minor-free graphs Structural analysis of comma-free and mixed circular codes in genetic alphabets Charles Delorme Prize for outstanding thesis in Graph Theory (2019) Collaborations span institutions like ULB, G-SCOP, Inria, and cross-disciplinary fields from computer science to mathematical biology.

• Algorithms
• Combinatorics
• Complexity Theory
• +۳ مورد دیگر

Sundar Vishwanathan is a Professor in the Department of Computer Science and Engineering at Indian Institute of Technology Bombay (IIT Bombay), where he has established himself as a leading researcher in theoretical computer science. His academic career spans over three decades with consistent contributions to algorithms, combinatorics, and complexity theory. His research interests form a cohesive body of work focused on theoretical foundations of computing: Algorithms (particularly approximation algorithms, online algorithms, and randomized algorithms) Combinatorics and extremal set theory Complexity theory and circuit lower bounds Graph theory and graph algorithms Combinatorial optimization problems Vishwanathan's publication record demonstrates remarkable consistency and depth, with publications spanning from 1990 to 2024. His recent work focuses on graph algorithms (particularly maximum matching problems), approximation algorithms for combinatorial optimization, and circuit complexity. He has developed innovative techniques using linear algebra, combinatorial methods, and probabilistic analysis to solve challenging theoretical problems. His approach often bridges theoretical insights with practical algorithmic considerations. His collaborative work spans multiple research groups, with frequent co-authorships with researchers such as Ashish Chiplunkar, Sumedh Tirodkar, and Sreyash Kenkre. His research has evolved from foundational work in online graph coloring in the 1990s to more specialized topics in combinatorics and recently to circuit lower bounds, showing both depth in core areas and adaptability to evolving research landscapes.

Terence Chi-Shen Taoمشاهده پروفایل

استاد

• Harmonic analysis
• Partial differential equations
• Algebraic combinatorics
• +۵ مورد دیگر

Terence Tao is an Australian-American mathematician and professor of mathematics at the University of California, Los Angeles (UCLA), where he holds the James and Carol Collins Chair in the College of Letters and Sciences. Widely regarded as one of the greatest living mathematicians, Tao has received numerous prestigious awards including the Fields Medal, the Breakthrough Prize in Mathematics, and the MacArthur Fellowship. Dr. Tao's educational background includes: Bachelor's and Master's degrees from Flinders University (1991) Ph.D. from Princeton University (1996) under Elias M. Stein Tao's research spans an extraordinary breadth of mathematical fields. He is particularly known for his work in harmonic analysis, partial differential equations, combinatorics, and analytic number theory. His research has included groundbreaking contributions to compressed sensing, the Green-Tao theorem on arithmetic progressions in prime numbers, and progress on the Navier-Stokes equations. Tao is renowned for his collaborative approach, having worked with over 60 co-authors throughout his career. Tao's publications demonstrate remarkable diversity across mathematical disciplines. His work shows strong trends in connecting seemingly disparate areas of mathematics, often bringing techniques from one field to solve problems in another. He has made significant contributions to both theoretical and applied mathematics, with applications ranging from signal processing to number theory. Among his numerous scientific achievements, Tao has received: Fields Medal (2006) Breakthrough Prize in Mathematics (2014) Royal Medal (2014) MacArthur Fellowship (2006) Crafoord Prize (2012) Princess of Asturias Award (2020) Tao has mentored numerous students throughout his career, with Monica Vișan among his doctoral students. He has secured significant research funding through prestigious awards including the Packard Fellowship, Sloan Fellowship, and Simons Investigator award. His collaborative research has been supported by multiple National Science Foundation grants. Tao maintains an active research group at UCLA and frequently collaborates with mathematicians worldwide. His blog and public lectures have made advanced mathematical concepts accessible to broader audiences, demonstrating his commitment to mathematical education and outreach.

Viresh Patelمشاهده پروفایل

مدرس

• Extremal combinatorics
• Probabilistic combinatorics
• Graph polynomials / partition functions
• +۵ مورد دیگر

Dr. Viresh Patel is a Lecturer in Optimisation at the School of Mathematical Sciences, Queen Mary University of London. His research focuses on extremal and probabilistic combinatorics, graph polynomials, phase transitions, and approximation algorithms. University: Queen Mary University of London School: School of Mathematical Sciences Email: viresh.patel@qmul.ac.uk Research Interests: Patel’s work bridges combinatorics, graph theory, and statistical physics. Key areas include Hamiltonian cycles in dense graphs, algorithmic approaches to graph polynomials, and complexity analysis of physical models. Publication Trends: Recent articles address structural graph theory (cycle/path decomposition), computational complexity (Potts/Ising models), and approximation algorithms using probabilistic and Taylor series methods. Collaborations: Regular co-authorship with researchers like Guus Regts, Allan Lo, and Matthew Jenssen indicates strong interdisciplinary engagement.

Jinho Baikمشاهده پروفایل

استاد

• Analysis
• Probability Theory
• Mathematical Physics
• +۷ مورد دیگر

Jinho Baik is a Professor of Mathematics at the University of Michigan, where he has been affiliated since 2002. His research spans analysis, probability theory, mathematical physics, and applied mathematics, with specific focus areas including random matrix theory, integrable systems, random permutations, and random growth models. He holds a Ph.D. from the Courant Institute of Mathematical Sciences (1999) and previously served as a Veblen Instructor/Assistant Professor at Princeton University (1999–2003). Education: B.S., KAIST, Korea (1995) Ph.D., Courant Institute of Mathematical Sciences, NYU (1999) Research Interests: Baik applies analytic tools to study probabilistic models, including eigenvalues of large random matrices, randomly growing interfaces, vicious random walkers, and longest increasing subsequences of random permutations. His work intersects with complex analysis, asymptotic analysis, functional analysis, potential theory, and combinatorics. Scientific Awards: Sloan Doctoral Dissertation Fellowship Kurt O. Friedrichs Prize for an Outstanding Dissertation in Mathematics (NYU) AMS Centennial Research Fellowship Sloan Research Fellowship CMFT 2005 Award for an Outstanding Young Researcher 2016 Simons Fellowship Professional Roles: Baik has served as Director of Graduate Admissions in the Department of Mathematics. He is an Associate Editor for unspecified journals and has presented at major international conferences, including the 2022 International Congress of Mathematicians (Probability section).

Alexander Barvinokمشاهده پروفایل

استاد

• Computational Complexity
• Algorithms
• Algebra
• +۶ مورد دیگر

Alexander Barvinok is a Professor in the Department of Mathematics at the University of Michigan, Ann Arbor. His office is located in East Hall (4066 East Hall), where he has been conducting research and teaching advanced courses in computational mathematics since receiving his Ph.D. from Leningrad State University in 1988. Professor Barvinok's research focuses on computational complexity and algorithms in algebra, geometry and combinatorics. He is particularly interested in connections between various notions of phase transition in statistical physics, analytical properties of partition functions and computational complexity. His work bridges theoretical mathematics with practical computational approaches, exploring how physical phenomena can inform algorithmic design and analysis. His research spans convex geometry, combinatorial optimization, and the computational aspects of polynomial systems. His recent publications (2016-2024) demonstrate a consistent focus on partition functions, computational aspects of convex bodies, and approximation algorithms for counting problems. He has made significant contributions to understanding the zeros of partition functions in statistical physics models, developing efficient volume estimation algorithms for polyhedra, and creating polynomial-time approximation schemes for problems previously thought to be computationally intractable. His work frequently connects algebraic properties of polynomials with computational feasibility. Professor Barvinok has authored several influential textbooks including "A Course in Convexity" (AMS Graduate Studies in Mathematics, 2002), "Integer Points in Polyhedra" (Zurich Lectures in Advanced Mathematics, 2008), and "Combinatorics and Complexity of Partition Functions" (Springer, 2016). He regularly teaches advanced graduate courses such as Math 669 on specialized topics including "Combinatorics, Geometry and Complexity of Integer Points" and "Topics in Convexity," with his lecture notes often evolving into significant research contributions.

Michael A. Zazanisمشاهده پروفایل

استاد

• Applied Probability
• Queueing Systems
• Stochastic Simulation
• +۴ مورد دیگر

Michael A. Zazanis is a Professor in the Department of Statistics at the Athens University of Economics and Business (AUEB), where he has been a faculty member since 1997. He previously held positions as Assistant Professor at Northwestern University (1986-1993) and Associate Professor at the University of Massachusetts, Amherst (1993-1997). Dr. Zazanis received his Engineering Diploma from the National Technical University of Athens (1982), followed by an M.Sc. (1983) and Ph.D. (1986) in Applied Mathematics from Harvard University. His academic journey reflects a strong foundation in both engineering and theoretical mathematics. His research focuses on Applied Probability, Queueing Systems, Stochastic Simulation, and applications in Manufacturing and Risk Management. Over his career, his work has evolved from foundational perturbation analysis of queueing systems to contemporary research on age-of-information metrics in communication networks. His contributions span theoretical developments in stochastic processes and practical applications in production control systems and risk analysis. Dr. Zazanis has published extensively in leading journals including Journal of Applied Probability, Stochastic Processes and their Applications, Operations Research, Management Science, and Queueing Systems. His 1988 paper in Management Science on perturbation analysis for the M/G/1 queue is considered seminal in the field. Best Publication Award from the TIMS College on Simulation (1990) He has served in administrative roles including Graduate Program Director (2003-2006) and Head of the Statistics Department (2006-2008) at AUEB. Dr. Zazanis teaches undergraduate courses in Mathematical Methods, Stochastic Processes, and Probabilities, as well as graduate courses in Advanced Stochastic Processes and Operations Research. He is married to Corinna Anastassakou and has one son, Aristomenes.

Richard M. Karpمشاهده پروفایل

استاد

• Computational Biology
• Algorithms
• Genomics
• +۳ مورد دیگر

Richard M. Karp is a Professor at the University of California, Berkeley, holding the Class of 1939 Chair in the Department of Electrical Engineering and Computer Sciences within the College of Engineering. He has been affiliated with UC Berkeley from 1968-1994 and again from 1999 to present, and has also served as a Research Scientist at the International Computer Science Institute in Berkeley since 1988. His career spans over five decades, beginning with his time at IBM Research from 1959-1968. His educational background includes a Ph.D. in Applied Mathematics from Harvard University (1959), an S.M. in Applied Mathematics (1956), and an A.B. in Mathematics (1955), all from Harvard. Karp's research spans multiple domains with a focus on algorithmic methods in genomics and computer networking . His work integrates theoretical computer science with practical applications in biological systems. He has made significant contributions across three primary research areas: Biosystems & Computational Biology (BIO), Operating Systems & Networking (OSNT), and Theory (THY). His interdisciplinary approach connects computational theory with real-world problems in molecular biology, network design, and combinatorial optimization. His early work in theoretical computer science laid foundations for complexity theory, while his later work has focused increasingly on computational biology applications. His research has been supported through affiliations with the Center for Computational Biology (CCB), Industrial Engineering and Operations Research (IEOR), and the Simons Institute for the Theory of Computing (SITC). Among his numerous honors are the Turing Award, National Medal of Science, Kyoto Prize, Von Neumann Theory Prize, and membership in multiple prestigious academies including the U.S. National Academies of Sciences and Engineering, the American Philosophical Society, and the French Academy of Sciences. Karp has supervised thirty-six Ph.D. students throughout his career, contributing significantly to the development of new generations of computer scientists. His work has been supported by numerous research grants across theoretical computer science, computational biology, and network theory. He has been instrumental in establishing interdisciplinary research programs that bridge computer science with biological sciences. He has been actively involved with the International Computer Science Institute in Berkeley since 1988, contributing to research teams focused on theoretical computer science and its applications to biological problems. His work has often involved collaborative teams spanning multiple disciplines, particularly in the field of computational biology where computer scientists work alongside biologists and medical researchers.

Niv Buchbinderمشاهده پروفایل

استاد

• Combinatorial Optimization
• Online Algorithms
• Algorithmic Game Theory
• +۲ مورد دیگر

Prof. Niv Buchbinder is a faculty member in the Department of Statistics and Operations Research at the School of Mathematical Sciences, Tel Aviv University. His research centers on algorithmic solutions for combinatorial optimization in offline and online contexts, with significant contributions to primal-dual methodologies and algorithmic game theory. His academic background includes a Ph.D. in Computer Science from the Technion (2008) under Prof. Seffi Naor and an M.Sc. in Computer Science from the Technion (2003) under Prof. Erez Petrank. Key research areas encompass Combinatorial Optimization, Online Algorithms, Algorithmic Game Theory, Primal-Dual Methods, and Submodular Optimization, focusing on competitive analysis for problems like set cover, ad-auctions, and caching. Recent publications (2012-2015) reveal a concentrated effort in submodular optimization and online decision-making, with applications in advertising, resource allocation, and machine learning. These works consistently employ primal-dual frameworks to achieve strong competitive ratios in adversarial settings. Scientific recognition includes: Best Paper Award at ESA 2007 for “Online Primal-Dual Algorithms for Maximizing Ad-Auctions Revenue” Best Paper Award at FOCS 2011 for “A Polylogarithmic Competitive Algorithm for the k-Server Problem” No information is available regarding student advising or research grants. Similarly, details about laboratory facilities, research teams, or future projects are not provided in the source materials.

Wojciech Samotijمشاهده پروفایل

دانشیار

• Extremal Combinatorics
• Probabilistic Combinatorics
• Ramsey Theory
• +۳ مورد دیگر

Wojciech Samotij is an Associate Professor at the School of Mathematical Sciences, Tel Aviv University. His research focuses on extremal and probabilistic combinatorics, Ramsey theory, large deviation theory, and additive number theory. He has held positions including Junior Research Fellow at Trinity College, University of Cambridge (2010–2014), and Post-doctoral researcher at Tel Aviv University (2010–2011, 2012–2013). PhD in Mathematics (2010), University of Illinois at Urbana-Champaign, supervised by Jozsi Balogh M.Sc. in Mathematics and Computer Science (2007), University of Wrocław His recent work explores large deviation principles in random graphs, hypergraph containers, and extremal problems in probabilistic settings. Publications span journals like Annals of Probability , Duke Mathematical Journal , and Transactions of the American Mathematical Society , with key contributions to Ramsey-type theorems and entropy-based combinatorial analysis. He has supervised graduate students in topics ranging from additive combinatorics to random graph theory, and taught courses including Probabilistic Methods in Combinatorics, Graph Theory, and Discrete Mathematics. His email contact is samotij@tauex.tau.ac.il and samotij@post.tau.ac.il .

Sergei Korotkikhمشاهده پروفایل

استادیار مهمان

• Algebraic combinatorics
• Integrable probability
• Representation theory
• +۱ مورد دیگر

Sergei Korotkikh serves as a Morrey Visiting Assistant Professor in the Department of Mathematics at the University of California, Berkeley's College of Letters and Science. Appointed in 2023, he contributes to the department's research and teaching mission in pure mathematics. Dr. Korotkikh's research focuses on several interconnected areas of pure mathematics. His primary interests include: Algebraic combinatorics Integrable probability Representation theory Vertex models His work bridges combinatorial structures with probabilistic methods and algebraic frameworks, contributing to the understanding of complex mathematical systems through multiple theoretical lenses. These research areas represent cutting-edge developments in pure mathematics with connections to theoretical physics and statistical mechanics. Professor Korotkikh maintains an active research program while fulfilling his teaching responsibilities in the mathematics department. He can be reached at korotkikh@berkeley.edu in his office at 843 Evans Hall.

Vince Lyzinskiمشاهده پروفایل

دانشیار

• Statistical Network Inference
• Graph Matching
• Vertex Nomination
• +۶ مورد دیگر

Vince Lyzinski is an Associate Professor in the Department of Mathematics at the University of Maryland, College Park, with additional affiliations in the Applied Mathematics, Statistics, and Scientific Computation (AMSC) program. His work spans statistical network inference, graph matching algorithms, and machine learning applications to complex networks. Ph.D. in Applied Mathematics and Statistics (2013), M.S.E. (2011), and M.A. (2007) from Johns Hopkins University B.S. in Mathematics from University of Notre Dame (2006) His research focuses on statistical network inference , particularly graph matching and vertex nomination , with applications to connectomics and adversarial activity analytics. Supported by DARPA MAA , AFOSR , and JHU HLTCOE , he develops algorithms like iGraphMatch for graph alignment and analysis. Recent publications (2025-2023) explore vertex label error impacts, network trimming for robustness, and stochastic blockmodel extensions. Key papers include "Asymptotically Perfect Seeded Graph Matching" and "ACRONYM: Augmented Degree-Corrected Network Models" . Scientific Awards : Best Paper Award at GTA3 2018 Workshop He has advised 13 Ph.D. students and 1 postdoc across UMD , JHU , and Texas A&M , including Keith Levin (now at UW-Madison) and Jesus Arroyo (now at Texas A&M). His DARPA-funded team includes researchers from UMass, UMD, BU, and JHU.

Steve Butlerمشاهده پروفایل

استاد

• Spectral Graph Theory
• Enumerative Combinatorics
• Mathematics of Juggling
• +۳ مورد دیگر

Steve Butler is a Professor and the Barbara J. Janson Professor in the Department of Mathematics at Iowa State University. His research focuses on spectral graph theory, combinatorics, and the mathematics of juggling. He earned his Ph.D. from UC San Diego (2008) under Fan Chung and completed an NSF Postdoc at UCLA with Benny Sudakov. Research Interests: Spectral Graph Theory: Studies eigenvalues of graph matrices, equitable partitions, and Laplacian properties. Recreational Mathematics: Explores mathematical patterns in juggling, card shuffling, and performance. Combinatorics: Investigates Eulerian numbers, distance matrices, and graph automorphisms. Scientific Awards: Barbara J. Janson Professorship (2017) LAS Early Achievement in Teaching Award (2014) Cassling Family Faculty Award (2014) MAA Iowa Section Award for Distinguished Teaching (2015) NSF Postdoctoral Fellowship (2008-2011) Advising and Outreach: Butler actively collaborates with students, producing educational videos and organizing undergraduate research initiatives. He served as Iowa State's calculus coordinator (2018-2020) and contributes to outreach programs for young STEM enthusiasts.

Andreas Galanisمشاهده پروفایل

دانشیار

• Algorithms
• Combinatorics
• Statistical Physics
• +۳ مورد دیگر

Dr. Andreas Galanis is an Associate Professor in Computer Science at the University of Oxford, where he serves as a Tutorial Fellow at Hertford College. He earned his PhD from Georgia Tech University in 2015 with a focus on Algorithms, Combinatorics, and Optimization. Research Interests: His work bridges approximate counting/sampling problems in computer science and phase transitions in statistical physics, with a strong emphasis on the computational complexity of partition functions, stochastic processes, and spin systems. Recent Publications: His research spans approximate counting algorithms ( ICALP 2019 , APPROX/RANDOM 2019 ), hardness results in Potts and random-cluster models ( Electronic Journal of Probability 2018 , APPROX/RANDOM 2018 ), and theoretical insights into correlation decay and spatial mixing ( SIAM Journal on Computing 2019 ). Scientific Awards: Best Paper Award, ICALP 2016 Collaborations: He frequently collaborates with researchers like Leslie A. Goldberg and Eric Vigoda on topics involving Markov chains, graph theory, and computational complexity.