جستجوی استاد راهنمای دانشگاهی

• Stochastic Analysis
• Stochastic Differential Equations
• Random Dynamical Systems
• +۳ مورد دیگر

Professor Michael Scheutzow is a distinguished faculty member at the Institute for Mathematics, Faculty of Mathematics and Natural Sciences, Technical University of Berlin. His academic career spans several decades with significant contributions to stochastic analysis and related fields. His research is widely recognized through numerous publications in top mathematical journals. Prof. Scheutzow's research focuses on stochastic differential equations, random dynamical systems, and stochastic processes. His work explores the intricate relationship between noise and system behavior, particularly examining how stochastic elements can both stabilize and destabilize dynamical systems. His research has important applications in mathematical physics, financial mathematics, and biological modeling. Analysis of his recent publications reveals a continued focus on fundamental questions in stochastic analysis, with increasing attention to singular stochastic differential equations, random attractors, and the interplay between noise and system stability. His work demonstrates remarkable consistency in addressing deep theoretical questions while maintaining connections to practical applications across multiple disciplines. Prof. Scheutzow has maintained an exceptionally productive research trajectory, with numerous publications appearing in leading journals such as Probability Theory and Related Fields, Annals of Probability, and Journal of Functional Analysis. His collaborations span the international mathematical community, reflecting his standing as a respected figure in stochastic analysis. He has supervised numerous doctoral students and collaborated extensively with researchers worldwide. His teaching responsibilities include advanced courses in probability theory and stochastic processes. While specific grant information isn't detailed in the available materials, his sustained publication record suggests consistent research support. Prof. Scheutzow's work forms a critical component of the stochastic analysis research group at TU Berlin, contributing to the department's international reputation in probability theory and its applications. His research continues to influence both theoretical developments and practical applications of stochastic methods across various scientific domains.

Bjoern Bringmannمشاهده پروفایل

استادیار

• Partial Differential Equations
• Mathematical Physics
• Probability Theory
• +۳ مورد دیگر

Björn Bringmann is an Assistant Professor in the Department of Mathematics at Princeton University. His research focuses on dispersive partial differential equations (PDEs), stochastic analysis, and mathematical physics, with a particular emphasis on invariant Gibbs measures and the well-posedness of nonlinear equations in high-dimensional settings. He holds a position in Fine Hall, Princeton’s mathematics building, and his work bridges deterministic and probabilistic methods in PDE theory. Bringmann’s research interests include the study of nonlinear wave and Schrödinger equations, topological solitons, and stochastic perturbations of geometric equations. He has contributed to understanding the dynamics of systems such as the sine-Gordon model, Yang-Mills equations, and the Abelian-Higgs model under stochastic forcing. His recent publications (2020–2025) explore invariant Gibbs measures for high-dimensional nonlinear wave equations, stochastic PDEs with space-time white noise, and the interplay between geometric structures and probabilistic methods. Notable themes include renormalization techniques, global well-posedness, and long-time behavior of solutions. Bringmann has no listed scientific awards in the provided texts. His advising and grants are not detailed here, but his affiliations include the Princeton Mathematics Department and associated research groups. He collaborates on projects involving stochastic analysis and nonlinear PDEs, though specific labs or teams are not mentioned.

Avi Mayorcasمشاهده پروفایل

مدرس

• Stochastic Analysis
• Partial Differential Equations
• Stochastic Processes
• +۳ مورد دیگر

Dr. Avi Mayorcas is a Lecturer in the Department of Mathematical Sciences at the University of Bath. His research focuses on stochastic analysis and partial differential equations, with particular interests in stochastic quantisation, regularisation by noise, and interacting particle systems. He has held postdoctoral positions at Technical University Berlin and the University of Cambridge as an INI-Simons Research Fellow. His teaching includes courses on Markov Processes and Applications. He is actively involved in projects such as 'FBSDE Methods in Heterogeneous Agent Models,' funded by the Engineering and Physical Sciences Research Council. His work bridges stochastic processes, PDEs, and applications in quantum field theory and chemotaxis models. Recent research explores topics like additive noise approximations in chemotaxis dynamics, fractional Brownian motion-driven systems, and singular SPDEs on Lie groups. He is open to supervising doctoral students in these areas.

David Ruiz Banosمشاهده پروفایل

دانشیار

• Insurance Mathematics
• Mathematical Finance
• Stochastic Analysis
• +۴ مورد دیگر

David Ruiz Banos is an Associate Professor at the University of Oslo's Department of Mathematics, part of the Faculty of Mathematics and Natural Sciences. His research focuses on stochastic analysis, mathematical finance, and actuarial science, with notable work on stochastic differential equations, fractional Brownian motion, and insurance mathematics. He has been affiliated with institutions including the University of Oslo and Inland Norway University of Applied Sciences, and held roles such as Adjunct Professor (20%). Education: PhD: Regularity of Stochastic Flows (2011-2015, University of Oslo) Master in Pure Mathematics (2010-2011, University of Barcelona) Bachelor in Pure Mathematics (2006-2010, University of Barcelona) Research Interests: Insurance mathematics (reserving, pricing), mathematical finance (pricing, hedging), stochastic analysis (SDEs, fractional Brownian motion, Malliavin calculus). His work bridges theoretical stochastic processes with practical applications in finance and insurance. Teaching: Courses include Probability Theory, Life Insurance and Finance, and Stochastic Processes, at the University of Oslo and Inland Norway University of Applied Sciences. Grants & Projects: SCROLLER (Norwegian Research Council, 2020-2024) focuses on stochastic control and machine learning for environmental risk models. He also leads the Insurance Seminar 2022 funded by Finansmarkedsfondet. Advising: Supervised PhD students like Oriol Zamora and Åsmund Hausken Sande, co-supervised Idunn Aamnes Mostue and Marc Lagunas, and mentored numerous master’s students. His guidance has produced impactful work in actuarial and financial mathematics. Publications: Over 20 peer-reviewed articles in journals like Advances in Applied Probability , Journal of Dynamics and Differential Equations , and Risks , reflecting his expertise in stochastic processes and their applications.

• Financial Mathematics
• Systemic Risk
• Energy Markets
• +۴ مورد دیگر

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.

Mathias Braunمشاهده پروفایل

مدرس

• Optimal Transport
• Metric Geometry
• Mathematical General Relativity
• +۷ مورد دیگر

Mathias Braun is a Lecturer at the École Polytechnique Fédérale de Lausanne (EPFL) , affiliated with the School of Basic Sciences and the Department of Mathematics. His research focuses on optimal transport, nonsmooth Lorentzian geometry, and applications to mathematical general relativity and partial differential equations. He has held prestigious positions including a Bernoulli Instructor fellowship and a Fields Postdoctoral Fellowship.

Karl-Theodor Sturmمشاهده پروفایل

استاد

• Stochastic Analysis
• Geometric Analysis
• Metric Measure Spaces
• +۷ مورد دیگر

Karl-Theodor Sturm is a Full Professor at the Institute for Applied Mathematics of the University of Bonn, where he has been since 1997. His research focuses on Stochastic Analysis , Geometric Analysis , and Metric Measure Spaces , particularly in the context of Ricci Curvature , Optimal Transport , and Liouville Quantum Gravity . He currently serves as Director of the Hausdorff Research Institute for Mathematics (since 2024) and previously coordinated the Hausdorff Center for Mathematics (2012–2019). As Principal Investigator of the ERC Advanced Grant Metric measure spaces and Ricci curvature - analytic, geometric and probabilistic challenges (2016–2022), he advanced interdisciplinary research at the intersection of geometry, probability, and analysis. Research Themes : Synthetic Ricci curvature, Optimal transport, Stochastic analysis on singular and random spaces, Dirichlet forms, Geometric inequalities. Recent Publications : Highlighted works include studies on heat flows on nonconvex domains, Wasserstein diffusions, and conformally invariant random fields in quantum gravity. His work often bridges probabilistic methods with geometric and analytic structures. Scientific Awards : Notable accolades include the Heisenberg Fellowship of the DFG (1994) and the ERC Advanced Grant (2016–2022). He has declined offers from prestigious institutions like Imperial College London, Northwestern University, and the University of Kansas. Teaching and Leadership : He leads graduate seminars (e.g., on Gaussian Free Fields and Liouville Quantum Gravity) and advanced courses in geometric analysis. As Managing Director of his institute and member of scientific committees (e.g., Mathematische Forschungsinstitut Oberwolfach), he shapes academic governance.

• Stochastic Control
• Optimal Investment
• Hedging in Incomplete Markets
• +۸ مورد دیگر

Michael Monoyios is an Associate Professor in Financial Mathematics at the University of Oxford's Mathematical Institute and a Tutorial Fellow in Applied Mathematics at Lady Margaret Hall. His academic journey includes a BSc and PhD in Theoretical Physics from Imperial College London and postdoctoral work at the Niels Bohr Institute. Prior to academia, he worked as a derivatives trader in London. His research centers on stochastic control and optimization in financial contexts, including portfolio management under transaction costs, hedging in incomplete markets, and dual methods for pricing. He also explores insider information, asymptotic expansions, and machine learning applications in finance. His publications predominantly focus on mathematical finance , with recurring themes of duality theory, optimal consumption, and dynamic programming. Recent work integrates neural networks for portfolio generation and investigates stability in recursive utility models. Awards and honors: Leverhulme Research Fellowship (2004-2005) University of Oxford Teaching Award (2007) Departmental Teaching Award (2014) He tutors undergraduates in Applied Mathematics and has organized workshops at the Isaac Newton Institute and London Mathematical Society.

• Stochastic Analysis
• Partial Differential Equations
• Mathematical Finance
• +۴ مورد دیگر

Nicolas Perkowski is a Research Group Leader at the Max Planck Institute for Mathematics in the Sciences, specializing in Stochastic Analysis , Partial Differential Equations , and Mathematical Finance . His work bridges theoretical and applied problems in stochastic filtering , rough paths , and singular SPDEs . Research Interests: His research focuses on stochastic partial differential equations with singular drift, homogenization for Lévy SDEs , and paracontrolled distributions for handling irregular data. He has contributed to nonlinear filtering in multiscale systems and stochastic calculus for financial modeling. Publications: His recent work includes fractional Kolmogorov equations , rough super Brownian motion , and Dean-Kawasaki SPDE approximations , reflecting expertise in probabilistic methods for PDEs and rough path theory .

Shigeki Aidaمشاهده پروفایل

استاد

• Probability theory
• Infinite dimensional analysis
• Malliavin calculus
• +۳ مورد دیگر

Shigeki Aida is a Professor at the Graduate School of Mathematical Sciences, The University of Tokyo . His research focuses on probability theory and its intersections with infinite-dimensional analysis , Malliavin calculus , rough paths , and stochastic differential equations . He has made significant contributions to understanding logarithmic Sobolev inequalities , semiclassical limits of Schrödinger operators , and stochastic analysis on loop spaces . Research Interests : Infinite-dimensional analysis arising from probability theory Applications of Malliavin calculus to stochastic processes Study of rough paths and their implications for differential equations Spectral gaps and asymptotic behavior on loop spaces Stochastic differential equations with reflecting boundaries Selected Scientific Awards : The Mathematical Society of Japan (MSJ) Analysis Prize (2007) Recent Trends in Publications : His recent work includes studies on rough differential equations with path-dependent terms , interpolation of discrete rough differential equations , and Hölder estimates for weighted sum processes . These publications emphasize stochastic analysis , infinite-dimensional spaces , and approximation methods .

Paul Glassermanمشاهده پروفایل

استاد

• Financial Engineering
• Risk Management
• Monte Carlo Simulation
• +۴ مورد دیگر

Paul Glasserman is the Jack R. Anderson Professor of Business at Columbia Business School's Decision, Risk, and Operations Division. He holds an AB from Princeton University (1984) and a PhD from Harvard University (1988). Previously, he worked at Bell Laboratories and held visiting positions at Princeton, NYU, and the Federal Reserve Bank of New York. His research spans: Advanced Monte Carlo methods for financial engineering Risk management frameworks for financial institutions Derivative pricing models and computational techniques Systemic risk and financial network analysis His publications focus on computational finance and risk measurement, with recent work exploring news-returns relationships, market stress indicators, and advanced simulation techniques for complex derivatives. Major awards include: Financial Engineer of the Year (2020) Lanchester Prize for best operations research publication (2006) Quant of the Year (2007) INFORMS Fellow (2008) Multiple teaching awards including Dean's Award for Teaching Excellence He chairs the Financial and Business Analytics Center in Columbia's Data Science Institute and serves on editorial boards of Operations Research, Mathematical Finance, and Stochastic Systems.

Daniel Conusمشاهده پروفایل

دانشیار

• Probability Theory
• Stochastic Modeling
• Financial Mathematics
• +۳ مورد دیگر

Daniel Conus is an Associate Professor of Mathematics and Associate Chair in the Department of Mathematics at Lehigh University. He co-directs the MS in Financial Engineering program, bridging academic research with practical financial applications. His research focuses on probability theory, stochastic modeling, and their applications in finance, economics, and catastrophe modeling. Dr. Conus has contributed to advancing methodologies in stochastic partial differential equations (SPDEs), quantization algorithms, and risk assessment for extreme events. Key research areas include: Probability Theory and Stochastic Processes Financial Mathematics and Derivatives Pricing Catastrophe Modeling and Resilience Assessment Intermittency Phenomena in SPDEs His recent work emphasizes the development of stochastic models for seismic hazards, ecological systems (e.g., bat carrying capacity in Africa), and financial derivatives. Collaborations with industry and interdisciplinary teams highlight his commitment to real-world applications of mathematical theory.

• Stochastics
• Actuarial Mathematics
• Numerical Mathematics
• +۳ مورد دیگر

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.

Aurélien ALFONSIمشاهده پروفایل

استاد

• Numerical Probability
• Financial Computing
• Mathematics
• +۳ مورد دیگر

Aurélien ALFONSI is a Professor at École des Ponts ParisTech and Director of the Centre d'Enseignement et de Recherche en Mathématiques et Calcul Scientifique (CERMICS). His primary affiliation is within the Mathematical and Computer Engineering Information department. He also holds a teaching position at the École Polytechnique, where he leads the Finance course in the Master’s program in Mathematics and Applied Computer Science. He specializes in Numerical Probability , Financial Computing , and Mathematics , with a focus on stochastic processes, optimal transport, and computational finance. His work bridges theoretical developments (e.g., Wasserstein distance analysis, stochastic Volterra equations) with practical applications in derivative pricing, risk management, and climate finance. ALFONSI’s research emphasizes high-order numerical schemes for stochastic models (e.g., Heston, Cox-Ingersoll-Ross processes) and innovative methods for solving complex financial and mathematical problems, such as the stochastic sewing lemma and dual approaches to option hedging. His contributions span Monte Carlo methods, volatility modeling, and the interplay between stochastic calculus and optimal transport theory. Though no specific awards are listed, his prolific publication record reflects sustained academic engagement. His role as department director suggests leadership in research and educational initiatives. He actively collaborates on projects involving financial engineering, climate derivatives, and computational methods for stochastic systems.

Ludovic Goudenegeمشاهده پروفایل

پژوهشگر ارشد

• Stochastic Partial Differential Equations
• Stochastic Processes
• Numerical Analysis
• +۳ مورد دیگر

Ludovic Goudenege is a CNRS Research Fellow assigned to the CentraleSupélec Mathematics Federation. His research focuses on stochastic partial differential equations (SPDEs), numerical analysis, and financial mathematics. He is affiliated with the Molecular and Macroscopic Energetics and Combustion Laboratory, contributing to interdisciplinary studies at the intersection of mathematics and applied sciences. His work spans theoretical and computational aspects of SPDEs, including stochastic processes, rare event simulation, and numerical methods for high-dimensional problems. Key applications include financial derivatives pricing (e.g., American options, variable annuities), risk management, and material science (e.g., phase field models for polymers). Collaborations with institutions like CNRS and CentraleSupélec underpin his interdisciplinary approach. Publications emphasize machine learning applications in finance, variance reduction techniques, and convergence analysis of numerical schemes for stochastic equations. Recent work explores non-Markovian systems, fractional noise models, and stochastic fluid dynamics. His research bridges fundamental mathematical theory with practical challenges in finance, engineering, and computational science.