Find Academic Supervisors

James ProppView profile

Professor

• Combinatorics
• Probability
• Dynamical Systems

James Propp, Ph.D., is a Professor in the Department of Mathematics & Statistics at the University of Massachusetts Lowell (Kennedy College of Sciences). He specializes in Combinatorics, Probability, and Dynamical Systems. His work bridges theoretical mathematics with applications in discrete structures and algorithmic processes. Propp has been actively mentoring undergraduates in research since joining UMass Lowell in 2006. Education: Ph.D., University of California at Berkeley Research Interests: Propp’s research focuses on tilings of geometric structures (e.g., Aztec diamonds, benzels), combinatorial dynamics, and homomesy phenomena. He explores probabilistic models in discrete systems and develops bijections for enumeration problems. His work often combines algebraic, geometric, and computational approaches. Recent Research Trends: Propp’s recent publications highlight advancements in tiling theory, toggle dynamics, and spectral analysis of combinatorial systems. His work on homomesy elucidates invariant averages under dynamical actions, while his studies on chip-firing games and rotor-routers model probabilistic phenomena deterministically. Awards: No awards explicitly mentioned in the text. Advising & Grants: While no specific grants are listed, Propp is noted for encouraging undergraduate research participation in mathematics. His advising contributions are central to his role as a mentor. Labs/Teams: No specific labs or collaborative teams are detailed in the provided information.

Wouter KagerView profile

Assistant Professor

• Probability Theory
• Discrete Systems
• Random Spatial Behavior
• +6 more

Wouter Kager is an Assistant Professor of Probability in the Department of Mathematics at Vrije Universiteit Amsterdam. His research focuses on discrete systems with random spatial behavior, including lattice aggregation models, random fields, and self-interacting random walks. He is particularly interested in the limiting behavior and scaling limits of these models, contributing to Probability Theory and related disciplines. Research Interests: Kager's work explores the intersection of probability theory and statistical physics. His research includes studies on stochastic processes, lattice models (e.g., Ising model), aggregation phenomena, and network stability. He has developed tools for analyzing stochastic systems, such as rotor-router aggregation and diamond aggregation models. His recent work also addresses applications in coordination games and queueing theory. Tools & Contributions: Kager has created open-source tools for converting EPS/FIG files to PDF/PNG, optimizing n-up printing, and LaTeX presentation themes. These tools are widely used in academic and technical contexts. His contributions to computational methods reflect his interdisciplinary approach to research. Publications: His articles span topics like stochastic domination, critical phenomena in random graphs, and stability analysis of stochastic networks. Recent works include advancements in likelihood ratio analysis, convex cone theory, and coordination game models.

• Distributed Algorithms
• Networks
• Search Problems
• +7 more

Professor Leszek Antoni Gasieniec is a faculty member at the University of Liverpool, Department of Computer Science, since 1997. He became a full professor in 2003 and served as Head of Department from 2012 to 2015. His research focuses on distributed algorithms, networks, and search problems, with interdisciplinary and industrial applications. Education: PhD in Computer Science (1994, Warsaw University) Postdocs: Université du Québec à Hull (1994-1995), Max-Planck Institut für Informatik (1995-1997) Research interests include: Distributed Algorithms Network Communication Combinatorial Optimization Mobile Agent Systems Population Protocols Algorithmic Complexity Recent publications highlight advancements in population protocols, symmetry breaking, and polyamorous scheduling, reflecting trends in distributed computing and fault tolerance. His editorial roles include positions at LMS Journal of Computation and Mathematics and Journal of Discrete Algorithms . Professional activities encompass organizing conferences (e.g., ALGO 2010), serving on EPSRC College, and reviewing grants for international foundations. Teaching includes modules on discrete mathematics and algorithms for AI/data science.

• Probability Theory
• Discrete Probability
• Random Planar Maps
• +6 more

Daniel C. Jerison is an Assistant Professor in the Department of Mathematics and Statistics at the University of San Francisco, supporting undergraduate programs in mathematics and data science. His academic career demonstrates a strong trajectory through prestigious institutions including Stanford University, Harvard University, Cornell University, and Tel Aviv University. Education: PhD in Mathematics, Stanford University, 2016 BA in Mathematics, Harvard University, 2007 Dr. Jerison specializes in discrete probability theory with emphasis on the properties of random objects and convergence of Markov chain algorithms. His research explores random planar maps, abelian sandpiles, circle packing, and discrete complex analysis. His work bridges theoretical mathematics with applications in statistical physics, examining how complex systems evolve and reach equilibrium states through sophisticated probabilistic methods. His approach often combines combinatorial techniques with analytical methods to solve problems in discrete geometry and probability. Analysis of Dr. Jerison's publications reveals consistent focus on the intersection of probability theory and discrete structures. His research demonstrates sophisticated mathematical techniques for analyzing convergence rates in Markov chains, establishing geometric criteria for planar structures, and solving boundary value problems for discrete systems. The recurring themes across his work include probabilistic methods applied to geometric problems, the study of self-organized critical systems, and theoretical frameworks for understanding complex discrete phenomena. Dr. Jerison has extensive teaching experience across multiple institutions, teaching courses ranging from foundational calculus to advanced probability theory. He has taught Probability Theory I (Math 6710), Stochastic Processes (Math 4740), Linear Algebra for Engineers (Math 2940), Prove It! (Math 3040), and Applied Complex Analysis (Math 4220) at Cornell University, in addition to his current teaching responsibilities at USF. He has also been involved with prestigious summer programs for high school students including PROMYS and SUMaC, where he directed research labs and served on admission committees.

• Probability Theory
• Discrete Probability
• Markov Chains
• +6 more

Daniel Jerison is an Assistant Professor in the Department of Mathematics and Statistics at the University of San Francisco, where he supports undergraduate programs in mathematics and data science. He holds a PhD in Mathematics from Stanford University (2016) and a BA in Mathematics from Harvard University (2007). His educational background includes: PhD in Mathematics, Stanford University, 2016 BA in Mathematics, Harvard University, 2007 Professor Jerison specializes in discrete probability theory, with research focusing on circle packing and discrete complex analysis, abelian sandpile and rotor-router models, and Markov chain convergence and mixing times. His work bridges theoretical mathematics with applications in statistical physics and computer science. His research demonstrates a consistent trajectory toward understanding the properties of random objects and stochastic processes, particularly in discrete settings where traditional continuous methods face challenges. His scholarly contributions include publications in prestigious journals such as Advances in Mathematics, Communications in Mathematical Physics, and Transactions of the American Mathematical Society, reflecting his significant contributions to the field of probability theory. Professor Jerison has been actively involved in teaching a wide range of mathematics courses including probability theory, stochastic processes, complex analysis, and linear algebra. He has also contributed to mathematics education through involvement with PROMYS and SUMaC, summer programs for high school students, where he directed research labs and served on admission committees. His academic journey includes postdoctoral positions at Cornell University working with Lionel Levine and at Tel Aviv University under Asaf Nachmias, establishing him within a strong network of researchers in probability theory and discrete mathematics.