Find Academic Supervisors

Christopher JanjigianView profile

Assistant Professor

• Mathematical Physics
• Probability Theory
• Random Walks in Random Environments
• +3 more

Christopher Janjigian is an Assistant Professor in the Department of Mathematics at Purdue University, specializing in probability theory with a focus on random walks in random environments, KPZ universality class phenomena, and stochastic partial differential equations. He holds a Ph.D. from the University of Wisconsin-Madison (2016) and has held postdoctoral positions at Université Paris Diderot (2016–2017) and the University of Utah (2017–2020). His research explores the infinite volume structure of random walks in random potentials and connections to models like the Kardar-Parisi-Zhang equation. He organizes the Purdue University Probability Seminar and has taught advanced courses including Stochastic Processes, Probability Theory, and Stochastic Calculus. His work bridges theoretical probability with applications in mathematical physics, emphasizing geometric and dynamic properties of stochastic systems. Recent research focuses on geodesic structures in percolation models, Busemann functions, and ergodic properties of directed polymer models. His articles investigate topics like exit point bounds in last-passage percolation and synchronization in the KPZ equation, contributing to the understanding of universal behaviors in stochastic systems.

Catherine WolframView profile

Assistant Professor

• Complex Analysis
• Probability Theory
• Geometry
• +3 more

Catherine Wolfram is a Gibbs Assistant Professor at Yale University and a National Science Foundation (NSF) postdoctoral fellow. She holds a PhD in Mathematics from MIT (2024), advised by Scott Sheffield, and an undergraduate degree from the University of Chicago. Her research focuses on complex analysis, probability, and geometry, with applications to statistical mechanics and random surfaces. She has presented her work at institutions including the University of Michigan, Columbia University, and international conferences such as the World Congress on Probability and Statistics. Her recent activities include talks on dimer models, holography in mathematical physics, and Teichmüller theory. Catherine has also engaged in teaching roles at MIT, leading recitations in probability and multivariable calculus. Her research has been supported by NSF postdoctoral funding, and her work has been published in journals like International Mathematics Research Notices (IMRN). She actively collaborates with researchers in probability, geometry, and mathematical physics, evidenced by her co-authored papers and participation in workshops at IHES, CIRM, and the Fields Institute.

• Probability Theory
• Large Deviations
• Harris Recurrent Markov Chains
• +12 more

Jeffrey F. Collamore is a full Professor at the Department of Mathematical Sciences , University of Copenhagen, specializing in Insurance and Economics (work area: IE). His research focuses on advanced probability theory and its applications in insurance mathematics and quantitative risk management. University of Copenhagen (2002-present) ETH Zurich (2000-2002) EURANDOM (1999-2000) Lund University (1998-1999) University of Illinois (1996-1998) Research Focus : Large deviations and rare event estimation Stochastic fixed point equations and random matrices Harris recurrent Markov chains Multidimensional ruin problems in insurance Stochastic simulation techniques Education : Ph.D. and MA in Mathematics, University of Wisconsin, Madison (Advisor: Peter Ney) BS in Physics and Mathematics, University of California, San Diego

Louis-Pierre ChaintronView profile

Research Fellow

• Probability Theory
• Stochastic Processes
• Optimal Transport
• +12 more

Louis-Pierre Chaintron is a Research Fellow at the École Normale Supérieure de Paris (ENS-PSL), affiliated with the Department of Mathematics and Applications (ENS DMA). His work bridges probability theory, stochastic processes, and applied mathematics, with a focus on constrained measure-valued dynamics, stability analysis, and approximation methods. PhD supervised by Julien Reygner (CERMICS) and Philippe Moireau (Inria M3DISIM) from 2022–2024 Member of the Probability and Statistics research team at ENS DMA Teaching roles include assistant for PDE courses and organizer of applied mathematics workshops Research Interests : His research explores connections between mean-field theory , large deviations , and viscosity solutions for Hamilton-Jacobi equations. He investigates filtering theory in dynamic systems, optimal transport frameworks, and Schrödinger bridge problems for high-dimensional systems. Applications span diffusion models in machine learning, muscle contraction modeling , and non-linear estimation under constraints. Recent Publications analyze convergence rates in stochastic control, stability of Gibbs principles, and jump-diffusion formalisms for biological systems. His work often combines calculus of variations , stochastic control , and viscosity solutions to address stability and approximation challenges. Contact : Email: lchaintron@dma.ens.fr Office: Bureau C11, Espace Cartan, 45 Rue d'Ulm, Paris

Samantha PettiView profile

Assistant Professor

• Bioinformatics
• Computational Biology
• Machine Learning
• +4 more

Samantha Petti is an Assistant Professor in both the Department of Mathematics (School of Arts and Sciences) and the Department of Computer Science (School of Engineering) at Tufts University. She is based at 177 College Avenue, Medford, MA. Her research focuses on computational biology, bioinformatics, and machine learning, with particular emphasis on protein structure analysis, genotype-phenotype mapping, and algorithm design for biological sequence analysis. She teaches courses such as Master's Thesis supervision, PhD Thesis guidance, and specialized topics in mathematics and computer science. Her work integrates interdisciplinary approaches, combining statistical methods, deep learning, and probabilistic models to address challenges in genomics, structural biology, and network science. Recent projects include developing end-to-end protein alignment tools and exploring sparse graph models for biological systems. Teaching: Supervises graduate thesis work (Master’s/PhD) and advanced courses in mathematics and computer science at Tufts. Lab/Team: Engages in collaborative research at the intersection of computational methods and biological systems.

Davide GabrielliView profile

Professor

• Statistical Mechanics
• Probability Theory
• Non-equilibrium Thermodynamics
• +2 more

Davide Gabrielli is a Full Professor in the Department of Information Engineering, Computer Science and Mathematics (DISIM) at the University of L'Aquila, Italy. His academic position focuses on Mathematical Physics with extensive contributions to statistical mechanics and probability theory. His primary research interests encompass Statistical Mechanics, Probability Theory, Non-equilibrium Thermodynamics, and Stochastic Processes. Gabrielli's work bridges theoretical mathematics with physical applications, particularly in understanding non-equilibrium systems, large deviations theory, and transport phenomena. His research demonstrates strong interdisciplinary connections between probability theory, statistical physics, and mathematical analysis. Analysis of his recent publications reveals consistent focus on non-equilibrium statistical mechanics, with particular emphasis on large deviation principles, stochastic processes on graphs, and thermodynamic uncertainty relations. His work frequently appears in leading journals including Journal of Statistical Physics, Physical Review Letters, and Communications in Mathematical Physics. Gabrielli maintains active collaborations with prominent researchers including L. Bertini, A. De Sole, G. Jona-Lasinio, and C. Landim, forming part of an influential research group in non-equilibrium statistical mechanics. His teaching responsibilities include advanced courses in Stochastic Processes, Probability Calculus, and Equations of Physics-Mathematics.

• Variational Calculus
• Partial Differential Equations
• Large Deviations Theory
• +3 more

Dr. Michiel Renger is a researcher at the Department of Mathematics, Technische Universität München (TUM), within the School of Computation, Information and Technology. His research focuses on variational calculus, partial differential equations, large deviations theory, non-equilibrium thermodynamics, and chemical reaction networks. He has contributed to advancing the understanding of macroscopic fluctuation theory, gradient flows, and their applications in stochastic systems. Teaching responsibilities include courses on higher mathematics for engineering students at TUM and specialized lectures on large deviations and convex analysis at TU Berlin. His work bridges theoretical mathematics with applications in physics and engineering, emphasizing interdisciplinary approaches. Renger’s publications span peer-reviewed journals in mathematics and physics, with a focus on rigorous probabilistic and analytical methods. He holds a PhD in Mathematics from Technische Universiteit Eindhoven (2013) and has collaborated on projects in collaboration engineering, addressing challenges in collaborative modeling and organizational design. His research also extends to applied problems like node counting in wireless networks and statistical consulting for industry.

Xiangfeng YangView profile

Associate Professor

• Large Deviations Theory
• Probability Theory
• Stochastic Processes
• +11 more

Xiangfeng Yang is an Associate Professor and Docent in the Department of Mathematics at Linköping University, Sweden, affiliated with the Division of Applied Mathematics (TIMA). His work bridges theoretical probability and applied mathematical modeling, with a strong emphasis on large deviations theory and stochastic processes. His research interests include: Large Deviations Theory Random Matrices and Sample Covariance Matrices Stochastic Bridges (Bernstein and Markov) Longest Runs, Gaps, and Extremal Eigenvalues Hidden Markov and Reciprocal Processes Modern Multivariate Statistical Analysis Xiangfeng Yang's recent publications (2015–2024) reflect a consistent and deep engagement with probabilistic asymptotics and extreme behavior in stochastic systems. His work spans theoretical foundations in probability, applications in random matrix theory, and modeling in financial and environmental contexts. A notable trend is his focus on large deviation principles across diverse settings—ranging from eigenvalues of random matrices to gaps in Poisson processes and runs in Markov chains. He frequently collaborates with PhD students, indicating an active supervisory role. His scientific contributions are published in reputable journals such as Journal of Applied Probability , Stochastic Processes and their Applications , and Theory of Probability and Mathematical Statistics . While no explicit awards are listed, the quality and volume of his output suggest recognition within the mathematical community. Xiangfeng Yang has supervised several PhD students to completion, including Joseph Okello Omwonylee, Denise Uwamariya, and Stefane Saize, with research topics closely aligned with his expertise in large deviations and stochastic modeling. His research likely involves collaboration within the Applied Mathematics division, though no specific lab or team name is mentioned. He continues to contribute actively to modern theoretical and applied probability.

Amir DemboView profile

Professor

• Probability
• Stochastic Processes
• Random Graphs
• +4 more

Amir Dembo is the Marjorie Mhoon Fair Professor in Quantitative Science at Stanford University, holding appointments in the Department of Mathematics and Statistics, and adjunct status in the Department of Electrical Engineering. His research focuses on probability theory and stochastic processes, with contributions to random graphs, large deviations, spin glasses, and statistical mechanics. He has authored numerous influential papers on topics such as random cluster models, Gibbs measures, and universality in interacting systems. Dembo teaches advanced probability courses (e.g., STAT310/MATH230) and has explored applications of probability in cryptography, including proof-of-stake blockchain protocols. His work bridges theoretical foundations with interdisciplinary applications in physics and computer science. Education details are not explicitly provided, but his academic trajectory is reflected through his professorial roles and research output. His research interests emphasize probabilistic analysis of complex systems, including random walks, graph structures, and non-equilibrium dynamics. Over 30 years of publications highlight his expertise in large deviation principles, asymptotic analysis of stochastic processes, and the interplay between combinatorics and probability. His recent work (2023–2025) explores cutting-edge topics like regular-tree-like graph models, flow-type scaling limits, and geometric area tilts in Brownian polymers. Earlier contributions include foundational studies on Erdős–Rényi hypergraphs, spectral measures of random matrices, and the universality of Langevin spin glass dynamics. While no explicit awards are listed, his prolific publication record and academic leadership reflect significant scholarly impact. Dembo’s advising and grants are not detailed here, but his teaching and research indicate mentorship in probability theory and stochastic modeling. Collaborations with interdisciplinary teams likely extend into areas like theoretical computer science and statistical physics.

• Ergodic Theory
• Dynamical Systems
• Probability Theory
• +6 more

Professor Ian Melbourne is a Professor of Mathematics at the University of Warwick's Mathematics Institute. His research focuses on ergodic theory, dynamical systems, and their links with probability and stochastic analysis. He holds editorial roles at *Ergodic Theory and Dynamical Systems* and *Probability Theory and Related Fields*. His work explores complex systems such as fast-slow dynamics, Lévy processes, mixing rates, billiards, and statistical limit laws. Research Interests: Ergodic theory, dynamical systems, stochastic processes, Lévy processes, mixing rates, billiards, and statistical laws. His contributions span theoretical frameworks for deterministic homogenization, superdiffusive limits, and large deviations in non-equilibrium systems. Publications highlight advancements in understanding systems with singularities, such as Lorentz gases and intermittent maps. His recent work emphasizes convergence to Lévy processes and polynomial decay of correlations in nonpositively curved surfaces. He has advised numerous PhD students (not listed here) and contributes to the academic community through editorial work and collaborative research. No specific grants or labs are detailed in the provided texts.

Jan SwartView profile

Professor

• Probability Theory
• Mathematical Statistics
• Interacting Particle Systems
• +5 more

Dr. Jan Swart is a Professor at the Department of Probability and Mathematical Statistics within the Faculty of Mathematics and Physics at Charles University, and a researcher at the Institute of Information Theory and Automation (UTIA) of The Czech Academy of Sciences in Prague. His office is located at Pod vodarenskou vezi 4, 18200 Praha 8, Czech Republic, where he works in Room 112 of the Stochastic Informatics department. Professor Swart specializes in probability theory with particular expertise in interacting particle systems, stochastic processes, and their mathematical foundations. His research spans quantum probability, random matrix theory, Markov chains, large deviations, and Brownian continuum objects. He has developed significant theoretical frameworks for understanding complex stochastic systems and their scaling limits. His publication record shows a consistent focus on advancing the mathematical understanding of interacting systems, with lecture notes and research materials that have become valuable resources in the field. His work demonstrates strong connections between theoretical probability and applications in statistical physics and theoretical biology. Professor Swart has supervised numerous theses and taught a wide range of advanced courses including Quantum Probability Theory, Interacting Particle Systems, Random Matrix Theory, and Advanced Markov Chains. He has also created a comprehensive simulation library for interacting particle systems that enables numerical exploration of these complex models. Notably, Professor Swart has also contributed to mathematical education through creative works including a mathematical fairy tale titled "Roulette a la princess" that explores game theory and probability concepts in an engaging narrative format. His work on the Czech language demonstrates his integration into the local academic community while maintaining international scholarly connections.

• Stochastics
• Limit Cycles
• Asymptotics
• +8 more

James MacLaurin is an Assistant Professor in the Department of Mathematical Sciences at New Jersey Institute of Technology (NJIT). His research focuses on stochastic processes, nonlinear dynamics, and their applications in mathematical biology and physics. Key areas include rare event analysis, stochastic hybrid systems, and the asymptotic behavior of complex systems. He has secured two grants from the Simons Foundation, supporting research in stochastic mathematics and interdisciplinary applications. His work bridges theoretical analysis with practical modeling, addressing challenges in wave dynamics under noise and phase reduction techniques. Recent contributions include studies on extreme first passage times, stochastic partial differential equations, and phase reduction of oscillatory systems. These publications highlight his expertise in applying advanced mathematical methods to understand real-world phenomena. Awards: Simons Foundation Award (2020–present) His research collaborations span applied mathematics, biology, and physics, with a focus on translating stochastic theory into computational tools for predictive modeling.

Thierry KleinView profile

Professor

• Probability Theory
• Statistics
• Uncertainty Quantification
• +4 more

Thierry Klein is a Professor at ENAC (École Nationale de l'Aviation Civile) since November 2016, previously serving as a Lecturer and Senior Lecturer at the Toulouse Institute of Mathematics from 2004 to 2016. His research focuses on probability theory, statistics, and uncertainty quantification, with a strong emphasis on sensitivity analysis (Sobol indices) and applications in aviation, environmental science, and mathematical modeling. He has co-supervised multiple doctoral theses, including those of Nabil Rachdi, Edouard Fournier, and José Bétancourt. His work includes contributions to Gaussian process regression, large deviation principles, and metamodeling techniques for coastal flooding prediction. Education: PhD in Probability and Statistics from the University of Versailles Saint-Quentin-en-Yvelines (2004), HDR (Habilitation à Diriger des Recherches) since 2016. His research interests span concentration inequalities, martingales, and random trees. Research Highlights: Developed methodologies for statistical inference in complex systems, including aircraft trajectory analysis and environmental reconstruction using pollen data. Contributions to open-source tools like the FunGp R package for Gaussian processes. International collaborations with institutions in Vietnam, Cambodia, and Colombia. Grants & Projects: Member of ANR-funded projects Riscope and Pepito . Scientific cooperation with the University of Havana and the Toulouse Mathematics Institute. Teaching & Roles: Responsible for the statistical research axis at ENAC's DEVI group. Contributed to doctoral training committees and administrative roles at ENAC.

Agoston PisztoraView profile

Associate Professor

• Probability Theory
• Statistical Mechanics
• Phase Transitions
• +3 more

Agoston Pisztora is an Associate Professor in the Department of Mathematical Sciences at Carnegie Mellon University, part of the Mellon College of Science. His research focuses on probabilistic analysis of random fields, disordered systems, and phase transitions, aiming to explain macroscopic phenomena through microscopic system analysis. He holds a Ph.D. from ETH Zürich and completed postdoctoral work at the Courant Institute, NYU. His research explores how random systems exhibit deterministic or stochastic behavior at macroscopic scales. Key areas include phase separation mechanisms in Ising models, percolation theory, and large deviation principles in disordered environments. His work bridges probability theory with statistical physics, addressing fundamental questions about self-averaging and critical phenomena. Notable publications include studies on Wulff crystals in Ising systems and surface-order large deviations. Though no awards or grants are explicitly listed, his contributions to phase transition theory and percolation are well-documented. No advising or lab affiliations are specified in the provided information.

Jay RosenView profile

Professor

• Probability Theory
• Stochastic Processes
• Gaussian Processes
• +6 more

Jay Rosen is a Professor in the Mathematics Department at The College of Staten Island, part of The City University of New York (CUNY). His research focuses on Probability Theory , particularly on Stochastic Processes including Markov Processes and Gaussian Processes , with a specialized interest in Local Times and Intersection Local Times . He also investigates Large Deviations and Multifractal Analysis of exceptional points in random processes. Current research explores connections between local times of Markov processes and Gaussian chaoses. Active work includes large deviations of intersection local times and analysis of exceptional points for random walks. Email: jrosen30@optimum.net