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Benjamin Landon is an Assistant Professor in the Department of Mathematics at the University of Toronto, where he has been faculty since 2021. His office is located in the Bahen Centre for Information Technology, Room 6264. Prior to joining the University of Toronto, he was a CLE Moore Instructor at the Massachusetts Institute of Technology from 2018-2021. Education: PhD in Mathematics, Harvard University (2018). Advisor: Horng-Tzer Yau M.Sc. in Mathematics, McGill University (2013). Advisors: Vojkan Jaksic and Robert Seiringer B.Sc., McGill University (2012) Dr. Landon's research focuses on Probability and Mathematical Physics, with particular expertise in Random Matrix Theory. His work spans various aspects of spectral statistics, eigenvalue distributions, and universality phenomena in random matrix ensembles. He has made significant contributions to understanding the behavior of extremal eigenvalues, linear spectral statistics, and connections to other areas of mathematical physics such as spin glasses and the KPZ universality class. His research often involves developing novel analytical techniques to establish precise asymptotic behavior in complex random systems. Analysis of Dr. Landon's publication record reveals a strong focus on the intersection of probability theory and mathematical physics. His work consistently explores universality phenomena across different random matrix ensembles and related stochastic systems. A notable trend is his investigation of connections between random matrix theory and other areas of mathematical physics, particularly spin glass models and the KPZ equation. His research demonstrates both technical depth in establishing rigorous asymptotic results and breadth in connecting seemingly disparate areas of mathematical physics.

• Probability
• Mathematical physics
• Random matrix theory
• +3 more

Alexey Bufetov is a Professor at Leipzig University, holding an ERC Starting Grant for his research in Integrable Probability (2022-2027). Previously, he served as a W2-Professor ("Bonn Junior Fellow") at the Hausdorff Center for Mathematics (2018-2021) and as a CLE Moore Instructor at Massachusetts Institute of Technology (2015-2018). His research centers on Probability Theory , with deep connections to Mathematical Physics and Combinatorics . Key areas include integrable probability, stochastic particle systems (ASEP/TASEP), random tilings, Schur generating functions, and representation-theoretic aspects of probability. His work often bridges abstract mathematical structures with physical models from statistical mechanics. Bufetov's recent publications reveal a strong focus on integrable systems and asymptotic analysis , particularly exploring connections between Mallows measures, vertex models, and random matrix theory. His 2025 work on Aztec diamond domino tilings exemplifies his signature approach combining combinatorial structures with probabilistic methods. His primary recognition is the ERC Starting Grant "Integrable Probability" (2022-2027), supporting his cutting-edge research program. Bufetov has maintained a prolific collaborative network, frequently publishing with leading researchers including Alexei Borodin, Vadim Gorin, Leonid Petrov, and Kailun Chen. His work appears in top journals such as Advances in Mathematics , Duke Mathematical Journal , and Communications in Mathematical Physics .

• Probability
• Mathematical Physics
• Combinatorics
• +2 more

Olya Mandelshtam is an Assistant Professor in the Department of Combinatorics and Optimization at the University of Waterloo . Her research focuses on algebraic combinatorics, particularly symmetric and quasisymmetric functions, with connections to probability and interacting particle systems. Education: Ph.D. in Mathematics, University of California, Berkeley (2016), advised by Lauren Williams Presidential Postdoctoral Scholar at UCLA (2016–2017) Tamarkin Assistant Professor and NSF Postdoctoral Fellow, Brown University (2017–2021) Research Interests: Her work bridges algebraic structures (e.g., Macdonald polynomials, Koornwinder polynomials) and probabilistic models like the asymmetric simple exclusion process (ASEP) and zero-range processes (TAZRP). She explores combinatorial bijections, multiline queues, and integrable systems to study these connections. Recent Activities: She organizes the Combinatorics Seminar at Waterloo and participates in conferences such as ICERM workshops on Category Theory and Machine Learning, ICECA, and events on integrable systems in algebraic combinatorics. Advising: Current graduate students include Kartik Singh, Jerónimo Valencia Porras, Guilherme Zeus Dantas e Moura, and Harper Niergarth, with past advisee William Chan. Research collaborations include work on multiline queues, non-attacking fillings, and particle system dynamics.

• Algebraic Combinatorics
• Enumerative Combinatorics
• Symmetric Functions
• +3 more

Yizao Wang is a Professor in the Department of Mathematical Sciences at the University of Cincinnati. He holds a Ph.D. from the University of Michigan (2012) and specializes in Probability Theory, Stochastic Processes, and their applications, with a focus on extreme value theory, long-range dependence, and random fields. His research includes studies on asymmetric exclusion processes, fractional Brownian motion, and self-similar processes. Education : Ph.D. in Mathematics, University of Michigan, 2012. Research Interests : Probability theory, stochastic processes, extreme value theory, long-range dependence, random fields, limit theorems, and applications in finance and statistics. His work frequently explores the interplay between combinatorial structures and stochastic models, such as random partitions and operator-scaling processes. Grants : He has led and co-led multiple grants, including: DoD Army Research Laboratory: Advances in Extreme Value Theory with Long-Range Dependence ($84,229, 2020–2023) National Security Agency: From Random Partitions to Self-Similar Processes ($40,000, 2016–2018) National Science Foundation: Cincinnati Symposium on Probability Theory and Applications ($20,000, 2018–2019) Students and Advising : Mentored numerous graduate and undergraduate students in capstone projects and independent studies, focusing on topics like random walks, branching processes, and stochastic differential equations. Notable advisees include Connor McClellan, Yiyang Yu, and Weiqing Yu (recipient of the Charles Phelps Taft Senior Thesis Fellowship). Labs/Teams : Active in collaborative research groups within the department, contributing to interdisciplinary projects in stochastic modeling and mathematical finance. Teaches advanced courses in probability, stochastic processes, and financial mathematics.

• Probability Theory
• Stochastic Processes
• Extreme Value Theory
• +4 more

Dominik Schmid is a junior professor at the University of Augsburg , affiliated with the Faculty of Mathematics, Natural Sciences and Technology and the Institute of Mathematics under the Chair of Stochastics and Its Applications . Prior to this role, he held postdoctoral positions at Columbia University , University of Bonn , and Princeton University . His research focuses on probability theory , particularly mixing times for Markov chains , limit theorems for interacting particle systems , and stochastic processes in random media . Education: PhD in Mathematics, Technical University of Munich (2021) Research Trends: His work on exclusion processes , random walks in dynamic environments , and fragmentation models has been published in leading journals such as Annals of Probability , Communications in Mathematical Physics , and Annals of Applied Probability . Key themes include non-equilibrium statistical mechanics , random graph dynamics , and hydrodynamic limits . Scientific Contributions: Developed sharp mixing time bounds for exclusion processes in various boundary conditions Extended cutoff theory to tree structures and Galton-Watson random media Derived limit profiles for the Bernoulli-Laplace urn and ASEP models Academic Engagement: Organizes workshops at institutions like the Simons Center and Oberwolfach , and has delivered invited talks in Poland , Hungary , Italy , and China . Teaches advanced courses on discrete probability and KPZ universality at the University of Augsburg.

• Probability Theory
• Markov Chains
• Interacting Particle Systems
• +3 more

Ivan Corwin is a Professor of Mathematics at Columbia University’s Faculty of Arts and Sciences and a member of the Data Science Institute (DSI). He holds affiliations with the Irving Center for Cancer Dynamics, Probability and Society Initiative, Program for Mathematical Genomics, and Quantum Initiative. He previously held positions at Microsoft Research, MIT, Institut Henri Poincaré (as a Poincaré Chair), UC Berkeley (as a Visiting Miller Professor), and others. His research focuses on probability and mathematical physics, particularly random interface growth, interacting particle systems, stochastic PDEs, and random matrix theory. Education: He earned his PhD from the Courant Institute in 2011. Research interests include KPZ universality, stochastic PDEs, and integrable probability models like ASEP, six-vertex models, and log-gamma polymers. His work bridges theoretical analysis and applications in non-equilibrium statistical mechanics, with recent trends emphasizing stationary measures, universality, and fluctuation behaviors. Awards include the 2021 Loeve Prize in Probability, 2018 Alexanderson Award, and fellowships from Clay Mathematics Institute, Packard Foundation, and Simons Foundation. He serves on scientific advisory boards for ICERM and MSRI. Advising and Grants: No specific grants or advisees listed, but his work has been supported by major fellowships. His affiliations with DSI and other institutes suggest involvement in collaborative projects. Labs/Teams: Active in interdisciplinary initiatives like the Probability and Society Initiative and Quantum Initiative, reflecting his focus on applying stochastic theory to complex systems.

• Probability Theory
• Mathematical Physics
• Random Matrix Theory
• +3 more

Wlodek Bryc is a Professor in the Department of Mathematical Sciences at the University of Cincinnati, affiliated with the College of Arts and Sciences. His research focuses on Probability Theory, including stochastic processes, non-commutative probability, random matrices, and large deviations. He has held grants from the National Science Foundation (NSF), Simons Foundation, and others, supporting work on topics like classical and non-commutative processes, KPZ equation, and exclusion processes. Roles: Professor, Taft Research Seminar organizer, editorial board member (Journal of Theoretical Probability, Demonstratio Mathematica). Grants: NSF DMS-0904747 (2009-2013), Simons Foundation #703475 (2020-2025), and others. Research interests include quadratic harnesses, ASEP, KPZ universality, and applications to statistical mechanics. He has organized the Cincinnati Symposium on Probability Theory and Applications multiple times and contributed to seminars on topics like free probability, random matrices, and limit theorems.

• Probability Theory
• Stochastic Processes
• Random Matrices
• +6 more

Konstantin Matetski is an Assistant Professor in the Department of Mathematics at Michigan State University (MSU). His research focuses on stochastic partial differential equations (SPDEs), integrable probability, and the KPZ universality class. He holds a Ph.D. in Mathematics from the University of Warwick (2016), advised by Martin Hairer, and a Master's from the University of Bonn (2012), advised by Anton Bovier. Prior to MSU, he served as an Ritt Assistant Professor at Columbia University (2018-2022) and a postdoctoral fellow at the University of Toronto (2016-2018). His work combines techniques from regularity structures, rough paths, and integrable systems to study non-equilibrium statistical mechanics and stochastic growth models. Recent publications include groundbreaking studies on the KPZ fixed point, TASEP dynamics, and stochastic PDE limits of particle systems. His research has been disseminated through top journals like Acta Mathematica , Probability Theory and Related Fields , and Annals of Probability . Teaching responsibilities include Complex Analysis (MTH-829) and Differential Equations (MTH-235) in Spring 2025. He actively contributes to academic communities via Google Scholar, ORCID, and arXiv profiles. No formal awards are explicitly listed, though his contributions to KPZ universality research are widely recognized in the field.

• Stochastic PDEs
• Integrable Probability
• KPZ Universality
• +3 more

Zhipeng Liu is an Associate Professor in the Department of Mathematics at the University of Kansas (KU), where he also served as Associate Chair and Director of Undergraduate Studies from July 2022 to December 2023. His research focuses on probability and mathematical physics, particularly on the analysis of random growth models, KPZ universality class phenomena, and their connections to integrable systems. He holds a PhD in Mathematics from the University of Michigan (2014), preceded by an MS from Peking University (2008) and a BS from Peking University (2004). Research Interests: Random growth models (TASEP, KPZ equations, last passage percolation) Universality in stochastic systems Integrable probability and asymptotic analysis Grants and Funding: NSF DMS-2246683 ($296,885, 2023-2026) NSF DMS-1953687 ($168,627, 2020-2024) Simons Collaboration Grant ($42,000, 2019-2024) AIM’s SQuaREs program (2024-2027) Professional Activities: Organizer of the KU Probability and Statistics Seminar Contributed to conferences such as the Mathematics in Industry Careers workshop

• Probability
• Mathematical Physics
• Random Growth Models
• +3 more

Craig A. Tracy is a Distinguished Professor Emeritus of Mathematics at the University of California, Davis. His primary affiliation is with the Department of Mathematics, where he has contributed extensively to mathematical physics, probability, and integrable systems. His research focuses on random matrix theory, statistical mechanics, and stochastic processes, particularly the asymmetric simple exclusion process (ASEP). He is renowned for co-developing the Tracy-Widom distribution, pivotal in random matrix theory and describing fluctuations in growth models and particle systems. Tracy's work often intersects with quantum mechanics and nonlinear equations, including studies on Bose and Fermi gases, Ising models, and Painlevé transcendents. His collaborations with Harold Widom have produced foundational results in integrable systems and their applications. His awards and honors, detailed in 'Awards.pdf', reflect his contributions to mathematical physics. He has advised numerous PhD students, many of whom now contribute to academia and industry. Tracy's research is supported by the National Science Foundation (NSF), and he maintains an active presence in lectures, courses, and publishing.

• Mathematical Physics
• Random Matrix Theory
• Statistical Mechanics
• +4 more

Jimmy He is an Assistant Professor in the Department of Mathematics at The Ohio State University (OSU). His research is supported by an NSF award (DMS-2451487). Prior to OSU, he was a CLE Moore Instructor at MIT, earned his PhD in Mathematics from Stanford University under Persi Diaconis, and studied Pure Mathematics and Statistics at the University of Waterloo. Education: PhD in Mathematics, Stanford University, 2021 Studies in Pure Mathematics and Statistics, University of Waterloo His research focuses on probability and combinatorics, with an emphasis on integrable probability, Markov chain mixing, symmetric functions, and discrete probability. He explores algebraic methods in these areas, particularly in the context of stochastic processes and combinatorial structures. Scientific Awards: NSF Award DMS-2451487 Advising and Grants: His research is supported by the NSF grant mentioned above. No specific advising roles or additional grants are detailed here. No specific labs or collaborative teams are listed in the provided information.

• Integrable Probability
• Markov Chain Mixing
• Symmetric Functions
• +3 more

Zhengye Zhou is an Assistant Professor in the Department of Mathematics at the University of Southern California Dornsife College of Letters, Arts and Sciences. Their research focuses on advanced mathematical frameworks for stochastic processes and quantum algebra. Research Interests: Interacting particle systems, orthogonal polynomial duality, asymptotic analysis, quantum groups, integrable systems, and non-commutative random surface growth. Their recent work explores asymptotics and duality in dynamic stochastic models, including multi-species asymmetric exclusion processes and higher-spin vertex models. This research intersects probability theory, mathematical physics, and algebraic structures. Zhengye Zhou contributes to theoretical developments in probability, particularly through rigorous analysis of Markov duality, unitary symmetries, and Gaussian fluctuations in stochastic systems. Notable publications span topics like dynamic ASEP, quantum group bialgebras, and boundary-driven exclusion processes.

• Mathematical Physics
• Probability Theory
• Stochastic Processes
• +3 more

Peter Nejjar is a Tenure-Track Juniorprofessor for Probability Theory at the University of Potsdam since January 2023. His research addresses fundamental questions of universality in stochastic systems , investigating why identical probability distributions emerge across disparate contexts like random matrices and growth models, with connections to combinatorics, mathematical physics, and numerical analysis. His primary research domains include stochastic particle systems (TASEP/ASEP), KPZ universality class phenomena, and Markov chain mixing behavior —particularly the cutoff phenomenon. Nejjar's work reveals deep structural parallels between shock fluctuations in interacting particle systems and random matrix eigenvalue distributions, leveraging connections to algebraic combinatorics through Schur processes. Recent publications demonstrate evolving focus from shock fluctuation theory (2015-2018) toward dynamical phase transitions in KPZ systems (2020-2022) and emerging interdisciplinary applications like DNA-based molecular tagging (2025). His collaborative network prominently features Patrik Ferrari across 7 publications, reflecting sustained focus on universality in exclusion processes.

• Probability Theory
• Stochastic Processes
• Mathematical Physics
• +3 more