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.
Peter K. Friz is Einstein Professor in Mathematics at Technische Universität Berlin (TU-Berlin), where he leads research in the Probability Theory and Mathematical Finance group. He is also affiliated with the Weierstrass Institute for Applied Analysis and Stochastics (WIAS), a major research institute in Berlin focused on applied mathematics. Professor Friz's research spans several interconnected areas of mathematics and finance: Stochastic analysis and rough path theory Quantitative finance and volatility modeling Partial differential equations driven by rough paths Applications of rough path theory to financial mathematics His recent publications demonstrate a continued focus on advancing rough path theory and its applications. The research trends show increasing sophistication in handling nonlinear rough differential equations, connections to machine learning through signature methods, and deeper applications to financial modeling, particularly in volatility analysis. His work bridges pure mathematical theory with practical financial applications, maintaining relevance to industry problems while advancing theoretical foundations. Professor Friz has received significant recognition for his work: ERC Starting Grant "Rough path theory, differential equations and stochastic analysis" (2010-2016) ERC Consolidator Grant "Geometric aspects in pathwise stochastic analysis and related topics" (2016-2021) Einstein Professorship at TU-Berlin He actively mentors students and researchers, with a list of former PhD students available through his academic profile. Professor Friz coordinates the DFG research unit "Rough paths, stochastic partial differential equations and related topics" (2016-2019) and has secured substantial funding from the European Research Council, DFG, and other sources. His research group regularly hosts seminars and collaborates with institutions worldwide, including recent invited courses at Cambridge, Paris (IHP), and Bonn (HIM). Professor Friz maintains active research collaborations through his position at TU-Berlin and affiliation with WIAS, contributing to Berlin's status as a hub for mathematical finance and stochastic analysis in Europe.
Şefika Kuzgun is a postdoctoral researcher at the Max Planck Institute for Mathematics in the Sciences (MPI MiS) in Leipzig, Germany, since September 2024, working in Prof. Dr. Felix Otto's research group. Previously, she held a postdoctoral position at the University of Rochester's Department of Mathematics. She earned her Ph.D. in Mathematics from the University of Kansas under Prof. David Nualart and completed her undergraduate studies at Boğaziçi University in Istanbul, Turkey. Research Interests: Her work focuses on stochastic analysis and stochastic partial differential equations (SPDEs) , particularly exploring properties of SPDEs using tools from stochastic calculus. She investigates applications of these equations to physical systems and employs techniques like the Malliavin-Stein method for density convergence and probabilistic approximations. Recent Activities: In 2025, she is organizing the Probability and Applied Analysis Summer School in Türkiye and has been an invited speaker at conferences such as the SPDEvent in Bielefeld and the Equadiff Conference in Karlstad. She also participates in initiatives like the Women in Mathematics group at the University of Rochester and cofounded a math support platform to assist Turkish students affected by the 2023 earthquakes. Service & Outreach: She co-founded the Directed Reading Program Türkiye and has mentored multiple students. She previously led the University of Kansas Student Chapter of the Association for Women in Mathematics, organizing professional development seminars and mentoring programs. She is dedicated to promoting equity in academia and community engagement through math outreach.
Prof. Dr. Christian Bender is a Full Professor of Applied Mathematics at Saarland University's Department of Mathematics since 2009. His academic career includes positions as Junior Professor for Applied Stochastics at TU Braunschweig (2006-2009) and various research roles at the Weierstrass Institute and University of Konstanz. He leads the Stochastics Group at Saarland University and serves on the Steering Committee of the DMV Specialist Group for Stochastics. His educational background includes: PhD in Mathematics (2003) from University of Konstanz Diploma in Mathematics (2001) from University of Konstanz Prof. Bender's research focuses on advanced stochastic methods with applications in mathematical finance. His work centers on backward stochastic differential equations, nonlinear option pricing, Monte Carlo methods for dynamic programming, and stochastic calculus for non-semimartingales. He has made significant contributions to fractional Brownian motion modeling, particularly in financial contexts where traditional semimartingale assumptions don't hold. His research bridges theoretical probability with practical financial applications, developing novel numerical methods for complex derivative pricing and risk management problems. Analysis of his recent publications reveals a strong trend toward developing computational methods for backward stochastic differential equations (BSDEs) and their applications in finance. His work increasingly integrates machine learning techniques with traditional stochastic methods, particularly in the 'Regression Anytime' approach. There's also a clear focus on non-semimartingale models that better capture market phenomena that standard models miss, along with continued exploration of fractional processes for modeling anomalous diffusion in financial markets. Prof. Bender has received the following professional recognition: Member of the Steering Committee of the DMV Specialist Group for Stochastics eV Associate editor of Mathematical Methods of Operations Research Associate editor of Methodology and Computing in Applied Probability Co-Organiser of the International Seminar on SDEs and Related Topics As an advisor, Prof. Bender has successfully guided seven PhD students to completion, including Matthias Thiel, Steffen Meyer, and Christian Gärtner. His research has been supported through various academic positions and collaborative projects with leading researchers in stochastic analysis and mathematical finance. His teaching portfolio includes advanced courses in stochastics, financial mathematics, and insurance mathematics across multiple semesters. Prof. Bender leads the Stochastics Group at Saarland University, which focuses on theoretical and applied stochastic analysis with particular emphasis on financial applications. The group maintains active collaborations with researchers worldwide and regularly contributes to major conferences in stochastic analysis and mathematical finance.
Borzykh Dmitry Alexandrovich is an Associate Professor at the National Research University Higher School of Economics , affiliated with the Department of Applied Economics under the Faculty of Economic Sciences . He also serves as a Research Fellow at the International Laboratory of Stochastic Analysis and its Applications . With 18 years of scientific and teaching experience since joining HSE in 2006, he teaches advanced courses in probability theory, mathematical statistics, and econometrics at both undergraduate and graduate levels. Education: Candidate of Physical and Mathematical Sciences (2022, HSE), Master's in Economics (2006, HSE), Bachelor's in Economics (2004, HSE) Key research areas: financial econometrics , stochastic analysis , structural breaks in time series , and stochastic volatility models His recent publications focus on stochastic processes , structural break detection , and quantile function applications across financial and economic modeling. He has received multiple institutional awards, including Best Teacher (2014, 2016–2025) and formal gratitudes from HSE departments (2019, 2022). He provides consultations via email and holds office hours at Pokrovsky Boulevard campus (room S517).
Romain Duboscq is a Lecturer at the National Institute of Applied Sciences (INSA) in Toulouse and a member of the Institute of Mathematics of Toulouse (IMT). He is affiliated with Paul Sabatier University (University Toulouse III) as evidenced by his contact information at the Toulouse Institute of Mathematics. His research spans several interconnected areas in mathematical physics: Analysis and numerical simulation of partial differential equations related to quantum mechanics Numerical methods for Gross-Pitaevskii type equations in Bose-Einstein condensates Stochastic Schrödinger equations and their numerical approximation Cauchy problem for stochastic Gross-Pitaevskii equations Stochastic regularization effects and the Itô-Tanaka trick Minimization of quantum entropies under local constraints Duboscq has developed the GPELab toolbox, a free-access Matlab resource for solving Gross-Pitaevskii equations, in collaboration with Xavier Antoine. His research often addresses challenging cases with strong nonlinearity and fast rotation in quantum systems. His work demonstrates a consistent integration of theoretical analysis with practical computational methods, making contributions to both fundamental understanding and applied methodology in quantum mechanical systems. His publication record shows sustained productivity across prestigious journals including Journal of Mathematical Physics, Annales Henri Lebesgue, and ESAIM: Mathematical Modelling and Numerical Analysis. The recent publications (2022-2025) demonstrate continued engagement with multiple research threads, particularly in quantum PDEs, stochastic methods, and computational approaches to quantum systems. Duboscq has received recognition through numerous publications in high-impact journals: Multiple publications in Annales Henri Lebesgue (2022) Work published in Journal of Mathematical Physics (2022) Contributions to ESAIM: Mathematical Modelling and Numerical Analysis (2025) Publications in Annals of Probability (2025) Research in Journal of Functional Analysis (2021) Duboscq maintains active research collaborations with several prominent researchers including Xavier Antoine, Christophe Besse, Renaud Marty, Anthony Réveillac, and Olivier Pinaud. His GPELab toolbox represents a significant contribution to computational tools for quantum physics research. While specific student supervision details aren't provided in the available information, his numerous collaborations suggest an active mentoring role in the research community. Duboscq leads the GPELab research group, which focuses on developing numerical methods and computational tools for quantum mechanical systems, particularly Bose-Einstein condensates modeled by Gross-Pitaevskii equations. The group's work bridges theoretical mathematics with practical computational applications, providing resources that enable more accurate simulations of complex quantum phenomena.
Martin Hairer is Professor of Pure Mathematics at École Polytechnique Fédérale de Lausanne (EPFL) in the School of Basic Sciences, Department of Mathematics, and also holds a position at Imperial College London. His research focuses on stochastic analysis, particularly stochastic partial differential equations, where he developed the groundbreaking theory of regularity structures. His research interests span multiple areas within stochastic analysis: Stochastic Partial Differential Equations (SPDEs) Regularity Structures and Renormalization Malliavin Calculus Rough Paths Theory Markov Processes and Ergodic Theory Hypoelliptic Operators Applications to Mathematical Physics Professor Hairer's publication record shows consistent high-impact contributions, with recent work focusing on singular SPDEs, quantum field theory models, and extending regularity structures to new contexts. His research has created fundamental connections between probability theory, analysis, and theoretical physics. Notable awards include: Fields Medal (2014) Fermat Prize (2014) Euler Medal (2014) Loève Prize (2014) Professor Hairer actively supervises graduate students and postdoctoral researchers, with funding available for both PhD and postdoc positions starting in 2026. He contributes to the academic community through the Probability and Stochastic Analysis Seminar at EPFL and by making his lecture notes publicly available. His dual appointments at EPFL and Imperial College London provide unique opportunities for international collaboration and student exchange.
