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Jan RosinskiView profile

Professor

• Probability Theory
• Stochastic Processes
• Stable and Infinitely Divisible Processes
• +3 more

Jan Rosinski is a Professor in the Department of Mathematics at the University of Tennessee. He holds a Ph.D. from Wrocław University (Poland). His research focuses on Probability and Stochastic Processes, particularly Stable and Infinitely Divisible Processes, Stochastic Analysis, and High-Dimensional Probability. He has held editorial roles at journals such as Probability and Mathematical Statistics and Discussiones Mathematicae - Probability and Statistics . His service includes managing editor roles and editorial board memberships across multiple international journals. Rosinski has contributed extensively to stochastic processes, Lévy processes, and infinitely divisible distributions, with a focus on theoretical developments and applications. Education: Ph.D., Wrocław University, Poland. Research Interests: Probability Theory, Stochastic Processes, Stable Processes, Infinite Divisibility, and High-Dimensional Probability. His work explores spectral representations, stochastic integrals, and applications in theoretical and applied probability. Editorial Contributions: Managing Editor of Probability and Mathematical Statistics (2008–present), and Editorial Board Member of Discussiones Mathematicae (2000–present), among others.

• 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.

• Stochastic Analysis
• Measure Theory
• Fractal Geometry
• +3 more

Alexander Steinicke is a Professor in the Department of Applied Mathematics, specializing in stochastic analysis and measure theory. His research bridges pure mathematics with applications in finance and fractal geometry, evidenced by 17 publications in high-impact journals including Advances in Mathematics and Stochastic Analysis and Applications . His primary research interests include: Stochastic Analysis Measure Theory Fractal Geometry Mathematical Finance Probability Theory Stochastic Processes Steinicke's recent work reveals a cohesive trajectory in geometric measure theory, particularly through his development of permeable set theory and its applications to fractal structures. His 2025 article on dimension theory represents a significant advancement, while his finance-related publications demonstrate practical extensions of stochastic calculus to market incompleteness problems. The integration of Lévy processes and semimartingale SDEs forms a consistent methodological thread across his publications. As an active academic, Steinicke regularly participates in conferences including the Austrian Stochastics Days and delivers invited talks on stochastic analysis. His consultancy work for research evaluations demonstrates engagement with broader academic governance.

• measure theory
• Gaussian measures
• measures on infinite-dimensional spaces
• +4 more

Egor Dmitrievich Kosov is an Associate Professor at the Faculty of Computer Science of the National Research University Higher School of Economics (HSE), where he has been working since 2016. He also serves as a Senior Research Fellow at the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis within HSE's Institute of Artificial Intelligence and Digital Sciences. Additionally, he holds positions as Senior Researcher at the Steklov Mathematical Institute's Department of Function Theory and Junior Researcher at the Laboratory of Multidimensional Approximation and Applications. Dr. Kosov earned his Candidate of Physical and Mathematical Sciences degree from Lomonosov Moscow State University in 2018, where he also completed postgraduate studies specializing in Mathematics and Mechanics with a qualification as Researcher. His research focuses on measure theory , particularly Gaussian measures , measures on infinite-dimensional spaces , logarithmically concave measures , and measurable polynomials . Kosov's work bridges theoretical mathematics with applications in stochastic analysis, exploring the regularity properties of distributions and developing discretization techniques for functional norms. His research has significant implications for understanding complex probabilistic structures in high-dimensional spaces. Analysis of Kosov's recent publications reveals a strong focus on polynomial mappings of random variables, particularly Gaussian and log-concave distributions. A significant portion of his research addresses discretization problems—developing methods to approximate continuous mathematical structures through discrete sampling. His publications demonstrate growing recognition in the mathematical community, with appearances in prestigious journals across multiple subfields of mathematical analysis. Letter of gratitude from the First Vice-Rector of HSE (March 2023) Letter of Gratitude from the Faculty of Computer Science at HSE (September 2019) Bonus for publication in List A journals (2023-2024) Multiple bonuses for international peer-reviewed publications (2019-2023) Best Teacher award (2018) Moscow Mathematical Society award (2021) At HSE, Kosov teaches Mathematical Analysis, Probability Theory, and Functional Analysis to undergraduate students in the Applied Mathematics and Computer Science program across both the Faculty of Computer Science and the Faculty of Economic Sciences. His teaching spans multiple academic years (2020-2023), demonstrating his commitment to education alongside research. Dr. Kosov is actively involved in research teams including the International Laboratory of Stochastic Algorithms and Multidimensional Data Analysis at HSE and the Laboratory of Multidimensional Approximation and Applications. His work connects with the broader mathematical community through collaborations with researchers such as V.I. Bogachev, V.N. Temlyakov, and others, contributing to Russia's strong tradition in mathematical analysis and probability theory.

• Stochastic Differential Equations
• Stochastic Variational Inequalities
• Monte-Carlo Methods
• +7 more

Aurel Rascanu is a full Professor at the Faculty of Mathematics, Alexandru Ioan Cuza University, Iaşi, Romania. His academic career, spanning since 1978, includes administrative roles as Dean of the Faculty of Mathematics (1990-1992) and Head of the Department of Applied Mathematics (2000-2004). He holds a PhD in Mathematics from his 1983 thesis on Qualitative problems for stochastic differential equations in Hilbert spaces . Research Interests: Stochastic differential equations, stochastic variational inequalities, numerical approximation methods, Monte-Carlo simulations, stochastic optimal control, and probabilistic techniques for partial differential equations. Scientific Activity includes over 30 years of invited lectures at international conferences (e.g., CIRM Marseille-Luminy, INRIA Rocquencourt) and collaborations with institutions across France, Italy, Poland, and Spain. He has published extensively on backward stochastic differential equations, fractional Brownian motion, and viability theory for moving sets. Teaching Contributions encompass courses in probability theory, Markov processes, stochastic analysis, Monte-Carlo methods, and numerical analysis. His work bridges theoretical mathematics with applications in economics and industrial problems.

Samy TindelView profile

Professor

• Probability
• Stochastic Analysis
• Stochastic PDEs
• +5 more

Samy Tindel is a Professor of Mathematics at Purdue University's Department of Mathematics, part of the College of Science. His research focuses on stochastic analysis, stochastic partial differential equations (SPDEs), fractional Brownian motion, rough paths theory, and applications to biosciences. He has published extensively in top-tier journals, with recent contributions including studies on Euler schemes for SDEs, ergodic properties of SPDEs, and rough path applications in image analysis. Teaching roles include courses such as Probability Theory, Stochastic Processes, and Multivariate Calculus from 2017 to 2025. His work bridges theoretical probability with practical applications, addressing challenges in numerical methods, stochastic modeling, and interdisciplinary problems. Publications span foundational topics like stochastic heat equations, Anderson models on Heisenberg groups, and novel numerical techniques for rough integrals. His research often emphasizes rigorous mathematical analysis and interdisciplinary relevance.

• Financial Mathematics
• Machine Learning
• Data Science

Dr. Huy Chau is a Lecturer in Financial Mathematics and Statistics at the Department of Mathematics, The University of Manchester. He holds a PhD from the University of Padova and Université Paris Diderot, under Professors Wolfgang J. Runggaldier and Peter Tankov. Prior to his current role, he worked as a Post-doctoral researcher at the Alfred Renyi Institute of Mathematics and as a Specially Appointed Assistant Professor at Osaka University's Center for Mathematical Modeling and Data Science. His research focuses on financial mathematics, machine learning, and data science. Key areas include stochastic processes, utility maximization, transaction costs modeling, and algorithmic trading strategies. His work often bridges theoretical advancements with practical applications in quantitative finance and statistical learning. Recent publications emphasize methodological innovations in pricing/hedging frameworks under market uncertainty, stochastic optimization techniques, and the analysis of behavioral investor dynamics in conic market models. Notable contributions address super-replication strategies under transaction costs and inversion-free numerical methods in statistical computing. Dr. Chau is actively involved in supervising PhD students and teaches advanced courses like Martingales with Applications to Finance. He collaborates internationally on projects such as the UoM-IISc Bangalore PhD initiative.

• Stochastics
• Actuarial Mathematics
• Numerical Mathematics
• +3 more

Michaela Szölgyenyi is a Professor at the Institute of Statistics within the Faculty of Technical Sciences at the University of Klagenfurt. Her research focuses on stochastic processes, numerical methods for stochastic differential equations (SDEs), and applications in financial and actuarial mathematics. She specializes in analyzing SDEs with irregular coefficients, including discontinuous drift and jump-diffusion dynamics. Her work emphasizes approximation methods for such equations, with contributions to optimal transport, risk theory, and insurance mathematics. She has published extensively on convergence rates, numerical schemes like the Euler-Maruyama and Milstein algorithms, and their applications in modeling financial and actuarial scenarios. She is affiliated with interdisciplinary projects and teaches courses related to stochastic analysis and mathematical finance.

Thomas CassView profile

Professor

• Probability
• Stochastic Analysis
• Mathematical Data Science
• +5 more

Thomas Cass is a Professor of Mathematics at Imperial College London, affiliated with the Department of Mathematics within the Faculty of Natural Sciences. He directs the EPSRC Centre for Doctoral Training (CDT) in Mathematics of Random Systems, a collaboration with the University of Oxford, and leads DataSig II, an EPSRC Programme Grant focusing on streamed data analysis using rough path theory. His research bridges pure and applied mathematics, emphasizing stochastic analysis, rough path theory, and their applications in machine learning and finance. He holds a PhD from the University of Cambridge and previously worked at the University of Oxford, where he was a Fellow and Member of Christ Church’s Governing Body. His academic leadership includes editorial roles at journals like the Journal of the London Mathematical Society and the Bulletin of the London Mathematical Society. He has organized major conferences on stochastic analysis and computational data science at institutions like the Oberwolfach Research Institute and ICERM. His work on signature methods from rough path theory has advanced mathematical data science, with applications in radioastronomy and molecular biology. Recent EPSRC grants support foundational research in rough analysis and its integration with Large Language Models. Cass’s contributions span theoretical developments, open-source software tools, and interdisciplinary collaborations.