جستجوی استاد راهنمای دانشگاهی

• Probability and Mathematical Statistics
• Stochastic Differential Equations
• Malliavin Calculus
• +۳ مورد دیگر

Michele Coghi is an Associate Professor in the Department of Mathematics at the University of Trento. He specializes in probability theory, stochastic differential equations, and their applications to mathematical physics and nonlinear partial differential equations. His research explores rough path analysis, mean-field models, and turbulence phenomena in fluid dynamics, with a focus on stochastic frameworks. He teaches courses such as Biostatistics and Statistical Methods across multiple departments, including the Department of Cellular, Computational and Integrative Biology (CIBIO) and the Department of Information Engineering and Computer Science. His work bridges theoretical mathematics with practical applications in data science and computational biology. Michele Coghi's recent publications emphasize rough stochastic calculus, nonlocal diffusion processes, and robust data assimilation techniques like ensemble Kalman filtering. His research on McKean-Vlasov dynamics and interacting particle systems highlights his contributions to stochastic modeling and mathematical fluid dynamics.

Purba Dasمشاهده پروفایل

مدرس

• Stochastic Analysis
• Mathematical Finance
• Financial Mathematics
• +۳ مورد دیگر

Dr. Purba Das is a Lecturer in Financial Mathematics at King's College London's Department of Mathematics, part of the Faculty of Natural, Mathematical & Engineering Sciences. She holds a DPhil from the University of Oxford (2022), specializing under Prof. Rama Cont, and prior degrees from Chennai Mathematical Institute (M.Sc. 2018, B.Sc. 2016). Her research focuses on stochastic analysis with applications to mathematical finance, particularly pathwise methods and rough volatility modeling. Education: DPhil (Oxford, 2022), M.Sc. (Chennai Mathematical Institute, 2018), B.Sc. (Chennai Mathematical Institute, 2016). Research interests include rough path theory, microstructure noise analysis, and functional Itô calculus. Recent work explores rough volatility's empirical validity, depositor behavior in digital banking systems, and Hölder continuity in function spaces. She co-organizes conferences on stochastic calculus and financial stability. Grants include £10k from KCL-IIT Madras Partnership and £5k from KCL's NMES Fund. Active in teaching financial mathematics and stochastic processes at both King’s and the University of Michigan.

Alexander Schiedمشاهده پروفایل

استاد

• Quantitative Finance
• Probability Theory
• Stochastic Analysis
• +۴ مورد دیگر

Alexander Schied is a Professor of Statistics and Actuarial Science at the University of Waterloo, holding the Munich Re Chair in Stochastic Finance and a University Research Chair. His research focuses on quantitative finance, probability theory, and stochastic analysis, with applications to risk measurement, financial modeling, and market microstructure. He co-authored the seminal textbook Stochastic Finance: An Introduction in Discrete Time (5th ed., 2025) and serves as Co-Editor of Finance and Stochastics . Before joining Waterloo, Schied held positions at the University of Mannheim, TU Munich, Cornell University, and TU Berlin. He earned his doctorate in mathematics from the University of Bonn. His work bridges theoretical advancements in stochastic processes with practical applications in finance, including robust optimization, model uncertainty, and high-frequency trading dynamics. His recent research explores rough volatility models , pathwise Itô calculus , and market impact games , with publications in top journals like Annals of Applied Probability and Mathematical Finance . His contributions to risk management and stochastic analysis have positioned him as a leading figure in mathematical finance. Awards & Roles: Munich Re Chair in Stochastic Finance (University of Waterloo) University Research Chair (University of Waterloo) Co-Editor, Finance and Stochastics Editorial Board Member: Applied Mathematics and Optimization , Mathematical Finance , and SIAM Financial Mathematics series Key Themes in Publications (2020–2025): Model-free portfolio theory and continuous-time optimization Rough stochastic volatility and Hurst parameter estimation Market impact dynamics and game-theoretic models Robust risk measures and optimization under uncertainty

• Probability Theory
• Stochastic Analysis
• Machine Learning
• +۵ مورد دیگر

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)

Nan Chenمشاهده پروفایل

استاد

• Financial Engineering
• FinTech
• Quantitative Finance
• +۷ مورد دیگر

Prof. Nan Chen is a Professor in the Department of Systems Engineering and Engineering Management at The Chinese University of Hong Kong (CUHK). He holds a PhD from Columbia University (2006) and directs the Bachelor of Engineering in Financial Technology (FinTech) and the Master of Science in Financial Engineering at CUHK-Shenzhen. His research focuses on quantitative finance, systemic risk, Monte Carlo simulation, and reinforcement learning. He has authored over 40 papers in top journals like Operations Research and Review of Financial Studies . Education : BSc and MSc in Probability & Statistics, Peking University (1998–2001) MPhil/PhD in Operations Research, Columbia University (2005–2006) Research Interests : FinTech and quantitative finance Systemic risk modeling and network analysis Reinforcement learning and stochastic control Monte Carlo methods and computational finance Awards & Grants : Best Student Paper Award, INFORMS (2006) Exemplary Teaching Awards (2009, 2017) Over HK$7M in GRF grants (2008–2023) Second Prize, Jiangsu Province Social Science Award (2023) Public Service : Member of Hong Kong's Payment Systems Tribunal and Insurance Appeals Tribunal, and reviewer for major journals like Mathematical Finance and Operations Research . Key Contributions : Pioneered Ito-Taylor expansions for diffusion models, dual methods for stochastic control, and liquidity risk frameworks. Led development of Hong Kong's first FinTech undergraduate program.

Julien Guyonمشاهده پروفایل

پژوهشگر

• Stochastic Volatility Modeling
• Nonlinear Option Pricing
• Optimal Transport
• +۳ مورد دیگر

Julien Guyon is a researcher at the Applied Probability team of CERMICS (École des Ponts ParisTech), where he joined in September 2022 after 16 years as a quantitative researcher in finance at Société Générale and Bloomberg L.P. He held adjunct professor roles at Columbia University and NYU’s Courant Institute (2015–2022), as well as at Université Paris Diderot and École des Ponts ParisTech. He serves as an Associate Editor for Finance & Stochastics , SIAM Journal on Financial Mathematics , and Journal of Dynamics and Games , and is a Louis Bachelier Fellow. Research Interests: Nonlinear option pricing, volatility modeling, optimal transport, numerical probability, and sports analytics (e.g., FIFA/UEFA competition design). Key Contributions: Developed a fairer FIFA World Cup draw method adopted by FIFA/UEFA, and pioneered joint calibration of S&P 500/VIX smiles using stochastic volatility models. His work bridges financial mathematics and real-world applications in sports. Awards: Louis Bachelier Fellow (2022). Advising & Grants: While specific grants or students are unmentioned, his research has significantly influenced financial markets and sports tournament design through collaborative projects with institutions like CERMICS and his industry work.

• Stochastic PDEs
• Regularity structures
• Coarse paths
• +۵ مورد دیگر

Mate Gerencser is an Associate Professor at Vienna University of Technology (TU Wien) within the Institute for Analysis and Scientific Computing (E 101). Since March 2024, he has been supported by an ERC Starting Grant. His research focuses on stochastic partial differential equations (SPDEs), regularization by noise, regularity structures, and numerical methods for stochastic equations. He leads projects such as the ERC StG Project on 'Stochastic PDEs and Renormalization' and the FWF START Project. His research interests span stochastic PDEs, including their theoretical foundations and numerical approximations. Key areas include the analysis of rough paths, KPZ-type equations, and the interplay between noise and equation regularity. Recent work emphasizes high-order approximation schemes and boundary renormalization techniques. Gerencser has received prestigious grants, including the ERC Starting Grant (2024), and has contributed to leading journals in probability and stochastic analysis. He teaches courses such as 'Stochastic PDEs' and 'Applied Mathematics Foundations' at TU Wien. His research group actively collaborates on projects addressing stochastic dynamics in continuous and discrete systems, with a focus on theoretical rigor and computational methods.

• Stochastic Analysis
• Rough Path Theory
• Mathematical Finance
• +۵ مورد دیگر

Paul GASSIAT is a Lecturer at CEREMADE (Paris Dauphine University-PSL) and DMA (École Normale Supérieure, Paris). His research focuses on stochastic analysis, particularly rough path theory, stochastic PDEs, and applications to financial mathematics. 2011 PhD in Mathematics, supervised by Huyên Pham 2019 Habilitation (Authorization to supervise research) Research interests include: Pathwise stochastic analysis and regularization by noise Stochastic Hamilton-Jacobi equations Rough volatility modeling Applications to financial mathematics and machine learning optimization Scientific roles: JUNIOR MEMBER OF THE UNIVERSITY INSTITUTE OF FRANCE (2024-2029) Co-head of the MASEF Master program (2023/2024) Member of the editorial board of EJP/ECP (2020-) Member of the RT Matrisk office (2024-)

• Stochastic Differential Equations
• Rough Paths Theory
• Malliavin Calculus
• +۳ مورد دیگر

Zachary Selk is a mathematician with expertise in stochastic analysis and its applications. Currently serving as a Dean's Postdoctoral Scholar at Florida State University (2025-present), he previously held the Coleman Assistant Professor position at Queen's University (2022-2025). Education: Ph.D. in Mathematics from Purdue University (2016-2022), advised by Ben McReynolds and Harsha Honnappa. Research Interests: Zachary's work focuses on stochastic ordinary and partial differential equations, rough paths theory, Malliavin calculus, and large deviations. His research extends to applications in physics, Gaussian measure theory, and analytic number theory. Publications Trends: Recent articles (2021-2024) highlight his contributions to stochastic solutions for hyperbolic PDEs, rough paths over fractal functions, and large deviation principles for Gaussian processes. Current work includes the Onsager-Machlup theory for Φ_d^4 models and stochastic calculus for Theta processes. Collaborations: Regularly partners with researchers like Francesco Cellarosi, Harsha Honnappa, and Abdol-Reza Mansouri on topics at the intersection of analysis and probability.

Nahid Jamshidiمشاهده پروفایل

پژوهشگر

• Stochastic Differential Equations
• Numerical Analysis
• Fractional Brownian Motion
• +۳ مورد دیگر

Nahid Jamshidi is a Researcher at the Institute of Mathematics within the Faculty of Natural Sciences II (Chemistry, Physics and Mathematics) at Martin Luther University Halle-Wittenberg, specializing in applied stochastics with emphasis on numerical methods for stochastic systems. Her research focuses on: Stochastic Differential Equations driven by fractional Brownian motion Numerical analysis of stiff and unstable systems Model order reduction techniques including Gramian-based and sampling methods Convergence and stability analysis of Euler, Milstein, and exponential integrators Computational challenges in high-dimensional stochastic modeling Long-range dependence phenomena in fractional noise processes Analysis of her 7 publications (2017-2024) reveals consistent advancement in numerical schemes for fractional SDEs, with recent work (2023-2024) addressing exponential integrators for stiff systems and empirical Gramian applications. Her research bridges theoretical stochastic analysis with practical computational solutions for complex noise-driven dynamics. No scientific awards are documented in available sources. The provided information contains no records of student supervision, grant funding, laboratory affiliations, or research team memberships.

• Stochastic Analysis
• Rough Path Theory
• Quantitative Finance
• +۵ مورد دیگر

Peter K. Friz is a Professor of Mathematics at the Technical University of Berlin and affiliated with the Weierstrass Institute for Applied Analysis and Stochastics . His research focuses on stochastic analysis , rough path theory , and quantitative finance , particularly volatility modeling . Key Affiliations: Institute of Mathematics, TU-Berlin Weierstrass Institute Major Grants: ERC Starting Grant (2010-2016) ERC Consolidator Grant (2016-2021) DFG Research Unit Coordination (2016-2019) Einstein Foundation Grant His work bridges rough path theory with stochastic differential equations and financial mathematics . Recent publications emphasize rough volatility models , nonlinear SPDEs , and pathwise analysis . He co-authored the book Multidimensional Stochastic Processes as Rough Paths with Nicolas Victoir. Scientific Awards: ERC Starting Grant ERC Consolidator Grant Einstein Professorship Friz has organized major conferences like 5ECM, SPA, and Newton Institute workshops. He has mentored PhD students in areas related to stochastic analysis and rough paths , though specific names are not listed here.

Salvador Ortiz-Latorreمشاهده پروفایل

دانشیار

• Stochastic Analysis
• Mathematical Finance
• Insurance and Risk
• +۸ مورد دیگر

Salvador Ortiz-Latorre is an Associate Professor in the Department of Mathematics at the University of Oslo, affiliated with the Risk and Stochastics research group and the completed project Stochastics of Renewable Energy Markets (STORE). He focuses on stochastic analysis, mathematical finance, and their applications in insurance, energy markets, and machine learning. His research integrates advanced stochastic processes, Malliavin calculus, and computational methods to model complex financial and actuarial systems. Key areas include pricing of insurance policies, stochastic volatility models (e.g., Heston-Hawkes), and numerical solutions for filtering problems. His work often bridges theoretical developments with practical applications, such as risk management and commodity market calibration. Recent articles emphasize innovations in variational methods for insurance pricing and spatial approximations of stochastic partial differential equations. Ortiz-Latorre collaborates extensively, with co-authors like David Baños and Dan Crisan. His contributions span high-impact journals like Stochastic Processes and their Applications and Advances in Applied Probability . Notably, he has pioneered techniques in particle filtering and neural network-based approximations for stochastic systems.

• Statistics
• Stochastic Processes
• Optimal Transport
• +۳ مورد دیگر

Dr. Benjamin Robinson is a Researcher at the Institute of Statistics within the Alpen-Adria-Universität Klagenfurt. His work focuses on advanced topics in stochastic processes, optimal transport, and mathematical analysis. He holds an MSc and MRes, and his research explores theoretical and applied aspects of probability theory, stochastic control, and optimization. Recent contributions include studies on adapted Wasserstein distances for SDEs, regularized Kellerer theorems, and optimal control of martingales in symmetric environments. While his publications span both theoretical and applied domains, no specific awards or advisee students are listed in the provided materials. His academic profile reflects a strong commitment to advancing stochastic methodologies and their interdisciplinary applications.

Torstein Kastberg Nilssenمشاهده پروفایل

دانشیار

• Nonlinear stochastic partial differential equations
• Rough paths
• Malliavin calculus
• +۲ مورد دیگر

Torstein Kastberg Nilssen is an Associate Professor in the Department of Mathematical Sciences at the University of Agder, Norway (since 2019). He has held postdoctoral positions at Technische Universität Berlin (2017-2019), University of Oslo (2014-2017), and guest researcher roles at University of Southern California (2015-2016), University of Manchester (2014), and Humboldt University Berlin (2013). Current affiliation: University of Agder Prior institutions: TU Berlin, University of Oslo, USC, University of Manchester, Humboldt University Berlin Research Focus: Nilssen's work revolves around nonlinear stochastic partial differential equations, rough path analysis, Malliavin calculus, and regularization effects of noise in systems with irregular coefficients. His research explores pathwise solutions for rough PDEs, stochastic transport equations, and robust methods in fluid dynamics under rough perturbations. Publication Trends: Recent articles (2019-2025) emphasize rough path applications to fluid mechanics, geometric rough paths in infinite dimensions, McKean-Vlasov dynamics for Kalman filters, and regularization of SDEs/PDEs with singular drift. Key keywords include stochastic differential equations, fractional Brownian motion, variational principles, and nonlocal diffusions.

Jevgenijs Ivanovsمشاهده پروفایل

دانشیار

• Stochastic Processes
• Probability Theory
• Mathematical Statistics
• +۳ مورد دیگر

Jevgenijs Ivanovs is an Associate Professor in the Department of Mathematics at Aarhus University . His research focuses on stochastic processes , particularly Lévy processes , extreme value theory , and probability theory , with applications in queueing systems , statistical inference , and multivariate extremes .