Find Academic Supervisors

Jean-François Le GallView profile

Professor

• Probability
• Brownian motion
• Lévy processes
• +8 more

Jean-François Le Gall is a full Professor at Université Paris-Saclay and a member of the Orsay Mathematics Laboratory (LMO) since 2006. He has held prominent positions at Pierre and Marie Curie University (1988-2006) and École Normale Supérieure (1997-2007). A Senior Member of the University Institute of France (2007-2017) and an elected member of the Academy of Sciences since 2013, he served as Vice-President of Research for the Mathematics Department at Orsay (2020–present) and led the ERC Advanced Grant GeoBrown (2017–2023). Education: Ecole Normale Supérieure (1978–1982), PhD in stochastic differential equations (1982), State Doctorate on Brownian motion (1987) Research Interests focus on probability theory , particularly Brownian motion , superprocesses , random trees , planar maps , and their connections to PDEs and geometric models. His work bridges stochastic analysis , branching processes , and coalescence phenomena . Selected Publications include foundational studies on the Brownian map , random geometry , and spatial branching processes . His 2025 paper on The area of spheres in the Brownian plane explores fractal properties of random metric spaces, while the 2020 Growth-fragmentation processes work links Brownian trees to fragmentation models. Scientific Distinctions : 1986 Rollo Davidson Prize 1997 Loève Prize in Probability 2005 Sophie Germain and Fermat Prizes 2019 Wolf Prize in Mathematics 2022 BBVA Frontiers of Knowledge Award Academic Leadership includes directing the Probability and Statistics Team (2013–2019) and the Master 2 in Probability and Statistics (2007–2015). He chairs editorial roles in Grundlehren der mathematischen Wissenschaften (since 2020) and Probability Theory and Related Fields (2005–2010).

Nicolas PerkowskiView profile

Professor

• Stochastic Analysis
• Probability Theory
• Stochastic Partial Differential Equations
• +3 more

Prof. Nicolas Perkowski is a Professor in the Department of Mathematics at Freie Universität Berlin, specializing in Stochastic Analysis and Probability Theory. He holds roles such as Vice Spokesperson of DFG CRC/TRR 388 (since 2024) and Chair of the Master of Mathematics Examination Board. His research focuses on stochastic partial differential equations (SPDEs), rough paths, and applications in mathematical physics. Notable contributions include work on singular SPDEs, fractional processes, and the KPZ equation. Perkowski has authored/co-authored numerous publications in top journals like the Annals of Probability and Communications in Mathematical Physics, and he serves as an associate editor for several journals. His institutional responsibilities include leadership in research collaborations and academic governance. Education: PhD and diploma in stochastic population models (details not explicitly provided in text). Research Interests: Stochastic Analysis, Probability Theory, SPDEs, Mathematical Physics, Nonlinear Filtering. Recent Articles: Focus on fractional processes, SPDEs with singular terminal conditions, and stochastic sewing lemmas. His work bridges theoretical probability with applications in physics and engineering, with a strong emphasis on rigorous mathematical frameworks for complex stochastic systems.

Andreas KyprianouView profile

Professor

• Pure and Applied Probability
• Branching Markov Processes
• Superprocesses
• +7 more

Professor Andreas Kyprianou is a leading probabilist at the University of Warwick's Department of Statistics, specializing in pure and applied probability. His research spans Branching Markov processes , Superprocesses , Lévy processes , and stochastic radiation transport . He directs the Centre for Mathematical and Computing Sciences (CAMaCS) and leads the £7.3M EPSRC MaThRad programme grant, focusing on nuclear technology applications. Academic Affiliations : University of Warwick (2023-present), University of Bath (2006-2023), Utrecht University (2001-2006), Heriot-Watt University, London School of Economics, University of Edinburgh Research Themes : Stochastic Modelling, Self-Similar Processes, Fragmentation-Coalescence, Neutron Transport, Monte-Carlo Simulation His recent work includes α-stable Lévy processes , jump SDEs in proton therapy , and non-local branching process stability . Scientific awards include the Royal Society Wolfson Merit Award and the Dutch Mathematics in Focus fellowship. He has supervised over 20 PhD students and co-led international research platforms like Prob-L@B and CIMAT-UNAM-Warwick-Bath . Grants section highlights major EPSRC and Royal Society funding for his interdisciplinary projects.

• Stochastic Partial Differential Equations
• Chemical Reaction Networks
• Malliavin Calculus
• +3 more

Dr. Panqiu Xia is a Lecturer in the School of Mathematics at Cardiff University since Autumn 2024. His research focuses on theoretical and applied probability, with expertise in stochastic partial differential equations (SPDEs) and chemical reaction networks (CRNs). Previously, he held postdoctoral positions at Auburn University (USA) and the University of Copenhagen (Denmark). He earned his Ph.D. in Mathematics from the University of Kansas (USA) in 2020 under the supervision of Professors Yaozhong Hu and David Nualart. His research interests include Malliavin calculus, Stein's method, and the analysis of SPDEs such as the parabolic Anderson model and super-Brownian motions. In CRN studies, he investigates stationary distributions and stochastic modeling of biochemical systems. His work bridges pure probability theory with applications in mathematical biology and statistical physics. Dr. Xia has taught courses in statistics, stochastic analysis, and numerical methods at institutions including Auburn University and the University of Copenhagen. His educational background includes a BSc from Wuhan University (China) and an MSc from the University of Macau. Key research themes include the long-term behavior of SPDE solutions, intermittency phenomena, and ergodic properties. His contributions span both theoretical advancements in stochastic analysis and applied problems in chemical reaction systems.

Steven EvansView profile

Professor

• Biostatistics
• Statistics
• Evolutionary Biology
• +7 more

Steven Evans is a Professor of Statistics at the University of California, Berkeley , affiliated with the College of Letters and Science and Department of Statistics. His research spans multiple domains of theoretical and applied probability. Biostatistics/Statistics Evolutionary Biology and Phylogenetics Population Genetics Random matrices and combinatorial structures Measure-valued processes and superprocesses Applications to biodemography, mathematical finance, and historical linguistics

• Probability Theory
• Combinatorial Probability
• Random Graphs
• +6 more

Dr. Minmin Wang is an Associate Professor in the Department of Mathematics at the University of Sussex, joining in 2019. Her research focuses on probability theory, including combinatorial probability, random graphs, branching processes, and scaling limits. She holds a PhD from Sorbonne University (formerly Paris 6), an MSc from École Polytechnique, and a BSc from Tsinghua University. Her work has been supported by an EPSRC New Investigator Grant (2023–2025). Notable achievements include the Itô Prize (2025) for her paper on stable trees. She serves on the editorial board of the Applied Probability Trust and the committee of the Royal Statistical Society's Applied Probability Section. Education: PhD in Probability, Sorbonne University (2014) MSc/Diplôme d'ingénieur, École Polytechnique, France BSc in Mathematics, Tsinghua University, China Research Interests: Combinatorial probability, random trees and graphs, scaling limits, fragmentation, coalescence, self-similar Markov processes, and superprocesses. Her recent work explores critical window phenomena in random intersection graphs and stochastic models in neutron transport. Grants & Awards: EPSRC Grant 'Stochastic processes on random graphs with clustering' (2023–2025) Itô Prize (2025) for 'Stable trees as mixings of inhomogeneous continuum random trees' Teaching & Supervision: Teaches courses like 'Linear Statistical Models' and 'Fundamentals of Mathematics'. Advises PhD student Félix Kahane and postdoc Alastair Haig. Supervised projects on Monte-Carlo methods and election forecasting. Research Group: Hosts a team exploring stochastic processes and random graph dynamics, with ongoing collaborations on neutron transport simulations and combinatorial tree structures.

• Probability Theory
• Stochastic Analysis
• Machine Learning
• +5 more

Xiong Wang is a J.J. Sylvester Assistant Professor in the Department of Mathematics at Johns Hopkins University (non-tenure track). His research focuses on probability theory, stochastic analysis, and their applications to machine learning and complex systems. He holds a Ph.D. from the University of Alberta (2022), where he received the Faculty of Science Dissertation Award. Education: Ph.D. in Mathematics, University of Alberta (2022) Advisor: Professor Yaozhong Hu Research Interests: Probability Theory & Stochastic Processes Machine Learning Algorithms Partial Differential Equations (PDEs) Interacting Particle Systems Inverse Problems in Stochastic Dynamics Mathematical Foundations of Data Science Teaching: Instructor for advanced courses at Johns Hopkins, including Stochastic Differential Equations, Partial Differential Equations, and Real Analysis Prior teaching roles at University of Alberta (Calculus, Linear Algebra) Recent Activities: Speaker at over 30 international conferences/seminars since 2021 Active in SIAM, CMS, and ICERM events Collaborations with institutions globally (e.g., Peking University, Brown University)

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.

Jaya BishwalView profile

Associate Professor

• Probability
• Stochastic Processes
• Statistical Inference
• +2 more

Dr. Jaya Bishwal is an Associate Professor in the Mathematics & Statistics Department at the University of North Carolina at Charlotte. Her research expertise centers on Probability and Stochastic Processes, with applications in mathematical finance and statistical inference. Her extensive publication record demonstrates deep specialization in fractional processes, stochastic differential equations, and statistical estimation methods. Recent work focuses on advanced econometric applications including Levy process modeling, fractional Ornstein-Uhlenbeck systems, and volatility estimation in financial mathematics. Methodological innovations include developments in quasi-likelihood estimation, Kolmogorov distance analysis for estimators, bootstrap methods for financial derivatives, and asymptotic theory for nonergodic systems. Her research bridges theoretical probability and practical applications in quantitative finance.

Jean VaillancourtView profile

Academic

• Probabilities
• Stochastic Equations
• Random Games
• +2 more

Jean Vaillancourt is an Affiliated Professor at the Department of Decision Sciences, HEC Montréal. He holds a PhD in Mathematics from Carleton University. His research focuses on stochastic processes, probability theory, and data mining applications in social media analysis. Key areas include martingale theory, random walks in random environments, and social network dynamics. He has contributed to methodologies for estimating multi-modal histograms and predicting network restructuring following node removals. His work integrates mathematical rigor with computational techniques, appearing in venues like Stochastic Analysis and Applications and Neural, Parallel and Scientific Computations . Active on platforms like Academia.edu and ResearchGate, his research bridges theoretical probability with practical applications in data science and network analysis. Education: PhD in Mathematics, Carleton University Research Interests: Probabilities, stochastic equations, social media analysis, data mining, and network science. His work emphasizes statistical methods for pattern extraction, centrality-based network prediction, and measure-valued processes. Articles Overview: Recent publications explore martingale convergence theories, random walk periodicity detection, and cascading removals in social networks. These studies highlight his dual focus on foundational probability theory and applied data-driven analysis. Grants & Advising: Supervision activities span the last five years, though specific grants or advisees aren't detailed here. His work on road database automation and edge detection showcases interdisciplinary impact. Labs & Teams: Collaborates across disciplines, integrating mathematical modeling with computational tools. Active in HEC's research ecosystem, contributing to formal concept analysis and SAR imagery processing initiatives.

Janos EnglanderView profile

Professor

• Spatial stochastic models
• Branching diffusions
• Superprocesses
• +2 more

Dr. Janos Englander is Professor of Mathematics at University of Colorado Boulder, holding a D.Sc. from Technion-Israel Institute of Technology (1997). His research focuses on spatial stochastic processes and their connections to partial differential equations. Key research areas include branching diffusions, superprocesses, processes in random media, and non-classical random walk models. Recent work examines spine decompositions in measure-valued processes and scaling limits of stochastic systems. Publications appear in Annals of Probability, Journal of Functional Analysis, and other leading mathematics journals. He serves on editorial boards and organizes conferences on stochastic processes.

• Stochastic Differential Equations
• Markov Processes
• Limit Theory
• +3 more

XIONG Jie is a Chair Professor in the Department of Mathematics at Southern University of Science and Technology (SUSTech), a position he has held since December 2017. His extensive career includes prior academic roles at the University of Macau (Professor, 2012-2017), University of Tennessee (Professor, 2004-2014; Associate Professor, 1999-2004; Assistant Professor, 1993-1999), and Peking University (Instructor, 1986-1988). He holds a Ph.D. in Statistics from the University of North Carolina at Chapel Hill (1992), an M.A. in Statistics from Peking University (1986), and a B.S. in Mathematics from Peking University (1983). His research focuses on stochastic analysis, including stochastic differential equations , Markov processes , limit theory , and applications in mathematical finance and control systems. Key areas involve nonlinear SPDEs, superprocesses, large deviations, and entropy dynamics, with interdisciplinary connections to physics and biology. Analysis of his recent publications (2012-2017) reveals a strong emphasis on stochastic partial differential equations (SPDEs) , optimal control theory , and measure-valued processes . Trends include applications in fluid dynamics, population control, financial modeling, and biological systems, utilizing advanced techniques in large deviations and stochastic filtering. Notable scientific awards include the Humboldt Research Fellowship (2003), Canada Research Chair in Stochastic Processes and Filtering (2002), and the University of Tennessee-Oak Ridge National Lab Science Alliance Research Award (1996-2000).

Renming SongView profile

Professor

• Probability Theory
• Stochastic Analysis
• Markov Processes
• +3 more

Renming Song is a Professor of Mathematics at the University of Illinois at Urbana-Champaign, with additional affiliations in the Department of Statistics. He holds a Ph.D. from the University of Florida (1993) and has been at UIUC since 1997. His research focuses on stochastic analysis, Markov processes, potential theory, and financial mathematics. Education: B.S. (1983), M.S. (1986) from Hebei University; Ph.D. (1993) from University of Florida. Affiliations: Professor in Mathematics and Statistics, UIUC; former visiting positions at Northwestern University and University of Michigan. Teaching: Currently teaches Math 461 (Probability Theory) and Math 432 (Set Theory and Metric Spaces). Past courses include Math 241 and Math 561. Research Interests: Song specializes in probability theory, with emphasis on Markov processes, branching processes, and their applications. Recent work includes studies on fractional Laplacians, supercritical killings, and asymptotic behaviors in branching systems. His research bridges stochastic analysis and potential theory, with contributions to non-symmetric jump processes and Dirichlet forms. Publications: Over 150 articles, including works in Communications in Mathematical Physics , Electronic Journal of Probability , and Journal of Functional Analysis . Recent topics include heat kernel estimates, tail probabilities, and asymptotic expansions in stochastic systems. Service: Member of NSERC’s Mathematics and Statistics Evaluation Group (2016–2019). Editorial roles at journals like Illinois Journal of Mathematics . Advisor to 11 Ph.D. students, including Hyunchul Park and Andrew Vlasic. Labs/Teams: Engaged in collaborative research on branching processes, stochastic dynamics, and financial mathematics through UIUC’s Mathematics Department and interdisciplinary initiatives.

William HancockView profile

Professor

• Cell and Molecular Bioengineering
• Biophysics
• Molecular Motors
• +4 more

William Hancock is a Professor of Biomedical Engineering (courtesy appointment) and Chemistry at Pennsylvania State University. His research focuses on molecular motors, microtubule dynamics, and enzymatic mechanisms for bioenergy production. He leads the Hancock Lab, which combines biophysics, biochemistry, and engineering to study motor proteins like kinesin and dynein, with applications to neurodegenerative diseases and biofuel development. Education: BS in Biomedical Engineering, Duke University (1988) PhD in Bioengineering, University of Washington (1994) Research Interests: Mechanochemical cycles of motor proteins Microtubule polymerization and transport Cellulase-driven biofuel production Single-molecule microscopy techniques Recent Articles: Focus on motor protein kinetics, microtubule dynamics, and cellulose degradation. Key themes include superprocessive motility of KIF1A, lignin inhibition of cellulases, and advanced imaging modalities like SCATTIRSTORM microscopy. Awards: Dean's Fellow, College of Engineering (2017) Advising & Lab: Mentors PhD, MS, and undergraduate students. Lab members have presented at BPS conferences and contributed to high-impact publications. The lab develops cutting-edge microscopes and collaborates on projects like enzyme optimization and motor protein simulations.

Vladimir VinogradovView profile

Professor

• Mathematical Finance
• Probability
• Probability Theory
• +6 more

Dr. Vladimir Vinogradov is a Professor of Mathematics at Ohio University and holds a status-only appointment at the University of Toronto, Scarborough (UTSC) within the Department of Statistical Sciences. He has affiliations with the Fields Institute as a Visiting Member and has previously served in administrative roles at Ohio University and UTSC. M.Sc. in Mathematics, Moscow State University Ph.D. in Probability and Statistics, Moscow State University Professor Vinogradov's research spans Probability Theory, Stochastic Processes, Mathematical Statistics, Financial Mathematics, and Mathematical Oncology. His work includes extensions of the Feller–Spitzer distribution, properties of Wright functions in probability, and stochastic models in tumor control probability. He has presented at numerous international conferences and contributed to the Fields Undergraduate Summer Research Program. Recent trends in his research include equi-/over-/underdispersion in probability distributions and connections between analysis and probability theory. He has also explored discontinuous superprocess approximations. Scientific Awards: NSERC International Research Fellowship BC Asia Pacific Scholars' Award Japan Society for the Promotion of Science Fellowship Professor Vinogradov has advised Actuarial Science and Mathematical Statistics majors and held leadership roles such as Chair of the Mathematics Promotion and Tenure Committee at Ohio University. He has served as an NSERC Canada external reviewer and graduate coordinator at the University of Northern British Columbia. He has co-organized international conferences, including the International Conference in Probability and Statistics 2022 and International Conference on Analysis, Applications, and Computations 2015 , and maintains an active research presence in probability and statistics.