Find Academic Supervisors

• Mathematical Statistics
• Stochastic Processes
• Extreme Value Theory
• +3 more

Patrik Albin is an Associate Professor in Mathematical Statistics at Chalmers University of Technology, affiliated with the Analysis and Probability Theory school. His research focuses on extreme value theory, stochastic processes, and probability theory. Role: Associate Professor (Mathematical Statistics) Institution: Chalmers University of Technology Unit: Analysis and Probability Theory Dr. Albin's work spans stochastic processes , particularly Lévy processes, Gaussian processes, and self-similar models, with applications in extreme value theory and statistical analysis. He investigates asymptotic distributions, tail probabilities, and upcrossing intensities, contributing to probability theory and stochastic calculus. His publications emphasize Lévy process extremes, covariance functions, and chi-type process limit theorems. Recent collaborations include K. Hamza and Fima C. Klebaner on stochastic calculus applications. Key scientific contributions include foundational work on Pickands constants, non-Brownian martingales, and random sum closures. He has no listed awards or students in available records.

• Stochastic Processes
• Fractional Calculus
• Statistical Inference
• +3 more

Prof. Kęstutis Kubilius serves as an Adjunct Professor within the Interdisciplinary Statistical Research Group at Vilnius University's Institute of Data Science and Digital Technologies. His research office is located at Akademijos Street 4, room 208 in Vilnius, with contact numbers +370 5 210 9731 and +370 618 06064. His research program centers on advanced stochastic processes, specializing in fractional Brownian motion and fractional stochastic differential equations (FSDEs). Key contributions include developing existence-uniqueness frameworks for FSDEs with stochastic forcing, creating numerical approximation schemes like the implicit Euler method, and advancing parameter estimation techniques for Hurst and Orey indices. His work bridges theoretical probability with applications in financial mathematics and statistical modeling of long-range dependent phenomena. Analysis of his 15 most recent publications reveals consistent focus on fractional calculus applications, with increasing emphasis on numerical methods since 2020. The research demonstrates methodological evolution from theoretical foundations toward practical implementation, particularly in volatility modeling and boundary-conditioned SDEs. Recent collaborative works (notably with Medžiūnas and Mishura) highlight interdisciplinary approaches combining statistical inference with computational mathematics. Prof. Kubilius actively supervises doctoral candidates, having guided Aidas Medžiūnas through completion of the 2018-2022 dissertation 'Parameter evaluation for mixed SDLs'. His research group maintains strong connections with international probability theory communities, evidenced by co-authorships with leading scholars in fractional calculus. As a core member of the Interdisciplinary Statistical Research Group, he contributes to Vilnius University's strategic focus on advanced statistical methodologies for complex data systems, with particular relevance to financial engineering and time-series analysis applications.

Kihun NamView profile

Lecturer

• Stochastic Analysis
• Backward Stochastic Differential Equations
• Financial Mathematics
• +3 more

Kihun Nam is a Lecturer in the School of Mathematics at Monash University, specializing in stochastic analysis with applications to mathematical finance and game theory. His research focuses on backward stochastic differential equations (BSDEs) and their applications in derivative pricing, stochastic control, and mean-field games. His primary research areas include: Backward Stochastic Differential Equations theory Stochastic control and optimization Financial mathematics and derivative pricing Mean-field game theory Machine learning for PDEs Nam's publications demonstrate deep expertise in BSDE theory, with recent expansion into financial applications and game-theoretic modeling. His work combines theoretical advances in stochastic analysis with practical applications in finance, particularly in pricing models and market microstructure. He has received multiple awards including the Academic Initiative Program Award (2015, 2016) and Samsung Scholarship for his PhD studies. Nam currently leads research projects including 'Can green investors drive the transition to a low emissions economy?' funded through Monash University's Centre for Quantitative Finance and Investment Strategies. His work involves collaborations across mathematical finance and sustainability economics.

Sühan AltayView profile

Researcher

• Financial Mathematics
• Quantitative Finance
• Stochastic Processes
• +5 more

Sühan Altay is a researcher affiliated with the Institute for Statistics and Mathematics at Vienna University of Economics and Business (WU Wien) . His work focuses on Financial Mathematics , Quantitative Finance , and Stochastic Processes , with applications to portfolio optimization, high-frequency trading, and partial information models. He has collaborated extensively with researchers like Prof. Zehra Eksi-Altay and Prof. Kenneth Colaneri. His research spans topics such as optimal trading strategies under uncertainty, market microstructure analysis, and the valuation of complex financial instruments. Notable contributions include studies on dark pools, pairs trading, and regime-switching models. His publications appear in journals like SIAM Journal on Financial Mathematics , International Journal of Theoretical and Applied Finance , and Annals of Operations Research . He holds a Dr.rer.nat. (PhD) in Statistics and Mathematics and has contributed to teaching materials, including lecture notes on stochastic differential equations. No awards or grants are explicitly mentioned in the provided texts.

John ArmstrongView profile

Professor

• Financial Mathematics
• Geometry
• Stochastics
• +3 more

John Armstrong is a Reader in Financial Mathematics at King's College London, based in the Department of Mathematics within the Faculty of Natural, Mathematical & Engineering Sciences. He holds a PhD in differential geometry from Wadham College, Oxford, and has extensive industry experience in financial computing, including co-founding Yolus and roles at Goldman Sachs. His research focuses on applications of geometry to stochastics, financial mathematics, and pension systems. Notable interests include information geometry, stochastic filtering, and twistor geometry. Education: BA and PhD in Mathematics from Wadham College, Oxford. Promoted to Senior Lecturer in 2017 and Reader随后. Professional Experience: Executive Director at Goldman Sachs, co-founder of Yolus, and former lecturer at Trinity College Oxford. Research interests span financial mathematics, geometry, and stochastics. Recent work includes collective pensions optimization, rough paths theory, and critical analyses of academic policies like Athena Swan. He has advised on pensions policy via projects funded by Nuffield Foundation and others. Publications highlight contributions to geometry-driven stochastic models, pension system design, and educational policy critique. His work bridges theoretical mathematics with applied finance and social science.

Martin HutzenthalerView profile

Professor

• Stochastic Analysis
• Numerical Methods for PDEs
• High-Dimensional Systems
• +2 more

Martin Hutzenthaler is a Professor in the Faculty of Mathematics at the University of Duisburg-Essen, leading the Stochastic Analysis research group. His office is located in Essen, Germany. His research centers on stochastic analysis and numerical methods, particularly multilevel Picard techniques for solving high-dimensional semilinear PDEs without the curse of dimensionality. Additional interests include stochastic differential equations, deep learning approximations, and functional inequalities. Recent publications focus on overcoming computational limitations in high-dimensional systems through novel approximation techniques. He serves as Associate Editor for the SIAM Journal on Numerical Analysis and offers student positions for tutorial development and algorithm implementation.

Xiaobing FengView profile

Professor

• Numerical Analysis
• Scientific Computing
• Partial Differential Equations
• +4 more

Xiaobing Feng is a Professor and Department Head in the Department of Mathematics at the University of Tennessee at Knoxville (UTK), where he has been since 1993. He holds a Ph.D. in Computational and Applied Mathematics from Purdue University (1992). His research focuses on numerical analysis, scientific computing, and partial differential equations (PDEs), with applications in biology, engineering, and physics. Key areas include stochastic PDEs, numerical methods for nonlinear PDEs, systems biology, and geometric flows. His work spans computational methods for stochastic systems, such as the elastic wave equations and Navier-Stokes equations, alongside contributions to fractional calculus and Sobolev spaces. He has developed innovative numerical techniques, including discontinuous Galerkin methods and efficient Monte Carlo algorithms for high-dimensional problems. His interdisciplinary research also addresses challenges in manufacturing metrology, such as geometric error identification in precision tools and biomedical imaging. Feng’s recent publications (2022–2025) emphasize stochastic modeling, numerical stability, and high-dimensional integration. He has contributed to data assimilation methods like i4DVar and advanced thermal modeling for machine tools. His research bridges mathematical theory with real-world applications, from medical devices to aerospace engineering. Notably, he has been recognized for his contributions to computational mathematics, though specific awards are not detailed. His academic leadership includes mentoring students and guiding departmental initiatives at UTK.

• Stochastic Analysis
• Rough Path Theory
• Stochastic Partial Differential Equations (SPDEs)
• +5 more

Paul GASSIAT is a Lecturer at CEREMADE (Paris Dauphine University-PSL) and DMA (École Normale Supérieure Paris). He holds a Junior Membership at the Institut Universitaire de France (IUF) from 2024 to 2029. His expertise lies in stochastic analysis, particularly rough path theory and its applications in financial mathematics and stochastic partial differential equations (SPDEs). He serves on the editorial board of Electronic Journal of Probability and ECP , and chairs the RT Matrisk office. Education : PhD in Mathematics (2011, supervised by Huyên Pham), HDR (2019). Research Interests : Pathwise stochastic analysis, singular SPDEs, rough volatility models, regularization by noise phenomena, numerical methods for stochastic systems. His work explores how irregular trajectories stabilize systems and applies to financial asset volatility modeling and neural network optimization. Articles Trends : Recent publications focus on rough volatility dynamics, stochastic Hamilton-Jacobi equations, and numerical schemes for SPDEs, emphasizing theoretical rigor and practical applications in finance. Awards : Junior Member, Institut Universitaire de France (2024–2029) Labs & Teams : Affiliated with CEREMADE and DMA. Active in collaborative projects involving rough path theory and financial mathematics.

• Stochastic Analysis
• Rough Path Theory
• Mathematical Finance
• +3 more

Dr. Christian Litterer is a Lecturer in the Department of Mathematics at the University of York. He holds a DPhil from the University of Oxford and has held postdoctoral positions at Oxford, Imperial College London, and École Polytechnique. His research bridges pure and applied mathematics, focusing on stochastic analysis, rough path theory, and their applications to mathematical finance and numerical methods. Key interests include Malliavin calculus, non-linear filtering algorithms, and pricing derivatives. He currently supervises Xintao Xie and leads a research group in Mathematical Finance and Stochastic Analysis. Dr. Litterer's work explores the intersection of geometric rough paths and probabilistic applications, with contributions to high-order numerical methods like cubature on Wiener space. He has been involved in organizing conferences (e.g., 2024 Conference on Modern Topics in Stochastic Analysis) and serves on the editorial board of Stochastic Analysis and Applications 2025 . His research projects are supported by grants such as the EPSRC-funded 'Making Cubature on Wiener Space Work.' Dr. Litterer’s academic activities include teaching roles in MSc programs like Financial Engineering and Mathematical Finance. His research collaborations span institutions globally, reflecting his interdisciplinary approach to stochastic analysis and its real-world applications.

• Stochastic Partial Differential Equations (SPDEs)
• Regularity Structures
• Rough Paths
• +3 more

Mate Gerencser is an Associate Professor at TU Wien's Institute of Analysis and Scientific Computing (E 101), supported by an ERC Starting Grant (2024). His research focuses on stochastic PDEs, regularity structures, rough paths, and numerical analysis of stochastic equations. He is located at Freihaus building, DA06L20, with contact via mate.gerencser@tuwien.ac.at. Research Interests: Stochastic Partial Differential Equations (SPDEs) Regularity Structures and Rough Paths Regularisation by Noise Phenomena Numerical Methods for Stochastic Equations Grants & Projects: ERC Starting Grant: "Stochastic PDEs and Renormalisation" (ID 101117125) FWF START Project: STA 119 FWF Stand-Alone Project: P 34992 ("Regularisation by noise in discrete and continuous systems") Teaching: Current: VU Applied Mathematics Foundations (WS 24/25), VO/UE Stochastic PDEs (SS 25) Past: Numerics of PDEs, Stochastic Differential Equations and Numerics Supervises bachelor/master theses

Aimé LACHALView profile

Lecturer

• Probability Theory
• Stochastic Processes
• Applied Mathematics
• +4 more

Aimé LACHAL is a Lecturer in Mathematics at the National Institute of Applied Sciences (INSA) Lyon, affiliated with the Camille Jordan Institute. His career spans over three decades, with prior roles at the University of Lyon and the School of Military Applications of Atomic Energy in Cherbourg (EAMEA). 1984: DEUG A in Structural and Material Sciences 1985: B.Sc. in Mathematics (summa cum laude) 1986: M.Sc. in Mathematics (summa cum laude) 1987: Mathematics Aggregation (14th rank) 1988: M.Sc. Research in Mathematics (DEA) 1992: Ph.D. in Probability (highest honors) 1995: Habilitation to Supervise Research in Mathematics His research focuses on Probability Theory and Stochastic Processes , particularly integrated Brownian motion, pseudo-processes governed by higher-order PDEs, and their applications to statistical testing, DNA replication, and quantum entropy. Key contributions include explicit distributions for sojourn times, first passage problems for non-Markov processes, and interdisciplinary work in Mathematical Biology and Random Walks . Publications cover advanced topics in Annals of Probability , Stochastic Processes and their Applications , and other high-impact journals.

Saul JackaView profile

Professor

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

Saul Jacka is a Professor at the University of Warwick and Editor-in-Chief of the journal Stochastics . As a Faculty Fellow at the Alan Turing Institute, his research focuses on probability theory with emphasis on mathematical finance, stochastic processes, stochastic control, optimal stopping, and probability on trees. He actively supervises PhD students in these areas and contributes to the Centre for Doctoral Training in Statistics. His research explores theoretical and applied aspects of stochastic analysis, including applications in financial markets and risk management. Recent work addresses optimal stopping problems, martingale approaches to control, and regime-switching models. Key contributions include studies on Snell envelope decomposition, coherent risk measures, and numerical approximations for Lévy processes. Jacka has held academic roles since the 1990s, publishing extensively in top journals such as Stochastic Processes and their Applications , SIAM Journal on Control and Optimization , and Annals of Applied Probability . His work frequently bridges theoretical probability with practical financial modeling challenges. Awards: Alan Turing Institute Faculty Fellowship Grants: Funded PhD places in Statistics (starting 2025) Labs/Teams: Centre for Doctoral Training in Statistics, Warwick Probabilistic Modelling Group

• Financial Mathematics
• Systemic Risk
• Energy Markets
• +4 more

Prof. Nils Detering is a Professor at the Chair of Financial and Actuarial Mathematics at Heinrich Heine University Düsseldorf. He previously held a tenure-track Associate Professor position at the University of California, Santa Barbara. His research focuses on systemic risk in financial systems, energy market modeling, and machine learning applications in finance. He has contributed over 20 peer-reviewed articles in top journals like Finance & Stochastics and SIAM Journal on Financial Mathematics . Education: PhD in Finance from Frankfurt School of Finance & Management and a Mathematics undergraduate degree from Georg-August University Göttingen. His work bridges stochastic analysis, random graph theory, and financial engineering. Key Research Areas: Systemic Risk, Energy Markets, Machine Learning in Finance, Random Graphs Teaching: Probability Theory, Stochastic Processes, Financial Mathematics courses at both undergraduate and graduate levels. Publications highlight methodological innovations in operator learning, reinforcement learning for financial systems, and quantitative risk management frameworks. His 2022 paper on reinforcement learning for banking networks earned a best paper award at the ACM International Conference on AI in Finance. Current affiliations include leading academic initiatives at HHU and collaborating on interdisciplinary projects combining mathematical finance with computational methods.

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.

• Nonlinear Partial Differential Equations
• Fluid Dynamics
• Geophysical Fluid Dynamics
• +3 more

Quyuan Lin is an Assistant Professor in the School of Mathematical and Statistical Sciences at Clemson University. He previously served as a Visiting Assistant Professor in the Department of Mathematics at UC Santa Barbara (2021–2023) and earned his Ph.D. from Texas A&M University under Dr. Edriss Titi. His research focuses on nonlinear partial differential equations (PDEs) in fluid dynamics, particularly geophysical and electrohydrodynamic contexts. Lin's work addresses theoretical and applied challenges in fluid mechanics, stochastic systems, and mathematical analysis of PDEs. Education: Ph.D., Texas A&M University, 2021 Research Interests: Lin investigates nonlinear PDEs arising in geophysical fluid dynamics, such as primitive equations and Navier-Stokes systems, with applications to electrohydrodynamics and stochastic processes. His studies emphasize well-posedness, singularity formation, and numerical approximation using methods like physics-informed neural networks. Key areas include fluid-structure interactions, anisotropic viscosities, and ergodic properties of stochastic fluid models. Publications Trends: Recent work explores stochastic regularization in inviscid systems, coarsening dynamics in phase separation models, and the interplay between transport noise and well-posedness in Boussinesq and Nernst-Planck systems. Lin also analyzes long-time behavior and ergodicity in coupled stochastic PDEs, contributing to both theoretical foundations and computational methods. Affiliations & Activities: Lin leads the Clemson Analysis & PDE Seminar and maintains an active research group focused on fluid dynamics and stochastic PDEs. His work bridges pure analysis and applied mathematics, with implications for climate modeling and material science.