David Landriault is a Professor in the Department of Statistics and Actuarial Science at the University of Waterloo, Canada, and a Canada Research Chair in Risk Theory. His research focuses on Actuarial Science, Quantitative Risk Management, Applied Probability, and Stochastic Processes, particularly in ruin theory, drawdown analysis, and stochastic control for insurance and finance applications. Education: PhD in Mathematics (2005), MSc in Mathematics (2003), BSc in Actuarial Science (2002) from Laval University. Affiliations: University of Waterloo (postdoctoral fellowship, 2006); Canada Research Chair in Risk Theory. Research Interests Risk and Ruin Theory Stochastic Control in Insurance and Finance Drawdown and Occupation Time Analysis Regime-Switching Models Reinsurance Design and Optimization Time-Dependent Risk Models Scientific Awards Fellow of the Canadian Institute of Actuaries (F.C.I.A.), 2009 Fellow of the Society of Actuaries (F.S.A.), 2006
Jin Ma is a Professor in the Department of Mathematics at the University of Southern California (USC), where he has served since 2007. He previously held professorships at Purdue University (1994–2008). His research focuses on stochastic analysis, stochastic differential equations, mathematical finance, and control theory. He directs USC's Mathematical Finance Program and serves on editorial boards for journals like Probability, Uncertainty and Quantitative Risk and SIAM Journal on Control and Optimization . Ma received his Ph.D. in Mathematics from the University of Minnesota (1992) and M.S./B.S. in Applied Mathematics from Fudan University (1985/1982). His work bridges theoretical stochastic analysis and applied domains like finance and insurance, with notable contributions to forward-backward SDEs and mean-field games. Research Highlights: Developed frameworks for stochastic control and backward SDEs in financial and insurance contexts. Advanced mean-field game models for limit order book dynamics and equilibrium analysis. Explored set-valued stochastic differential equations and their applications in risk management. Grants & Advising: Advised numerous graduate students in stochastic processes and mathematical finance. Research supported by NSF grants and industry collaborations.
Prof. Dr. Rudi Zagst is a Professor of Mathematical Finance at the Technical University of Munich (TUM), where he serves as Head of the Department of Mathematical Finance within the TUM School of Computation, Information and Technology. He has held this position since 2001 and is actively involved in teaching, research, and academic leadership. In 2003, he was appointed as a second member of the Faculty of Economics, and since 2004, he has served as Deputy Chairman of the joint elite degree program 'Finance & Information Management' of the University of Augsburg and TUM. Prof. Zagst earned his doctorate in business mathematics from the University of Ulm, where he later completed his habilitation in 2000. His academic journey began with a professional career at HypoVereinsbank AG, where he served as Head of Product Development in Institutional Investment Management before becoming Managing Director of RiskLab GmbH in 1997. His research focuses primarily on financial engineering, risk management, and asset management, with particular emphasis on portfolio optimization, mathematical finance, and quantitative risk management. His work bridges theoretical finance with practical applications, often incorporating advanced mathematical techniques to solve complex financial problems. Recent publications demonstrate his continued interest in GARCH models, portfolio optimization under various constraints, and the application of machine learning techniques to financial problems. Analysis of his recent publications (2024-2025) reveals a strong focus on portfolio optimization under complex market conditions, particularly using GARCH models to capture volatility dynamics. His work increasingly incorporates machine learning techniques (as seen in the credit spread analysis paper) while maintaining rigorous mathematical foundations. Many papers explore the intersection of theoretical finance with practical investment strategies, reflecting his commitment to bridging academic research with real-world financial applications. Professor of the Year 2007 (awarded by Unicum Profession magazine) Prof. Zagst has supervised numerous bachelor's, master's, and doctoral theses through TUM's Finance and Actuarial Science research group. His collaborative work with industry partners through the TUM CAIR Labs and RiskFactory demonstrates strong connections between academic research and practical financial applications. He has received research funding through various industry partnerships with major financial institutions including Allianz, Munich Re, and ERGO Group AG. Prof. Zagst leads the Research Group Finance and Actuarial Science at TUM, which includes Professors Matthias Scherer, Aleksey Min, and Christoph Knochenhauer. The group maintains strong industry connections through the TUM CAIR Labs initiative, collaborating with over 25 financial institutions including Allianz, Munich Re, Deloitte, PwC, and KPMG. Their RiskFactory laboratory serves as a bridge between academic research and practical financial risk management applications in the industry.
Academic Profile: Damir Filipovic is a Full Professor and the Swissquote Chair in Quantitative Finance at the College of Management of Technology (CDM) of École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He previously held academic positions at the University of Vienna, University of Munich, and Princeton University, and served as Head of the Vienna Institute of Finance. Research Focus: Quantitative finance, risk management, stochastic processes, term structure modeling, volatility risk, and machine learning applications in financial markets. Industry Collaboration: Co-developed the Swiss Solvency Test for insurance capital requirements while consulting for the Swiss Federal Office of Private Insurance. Publications: Contributed extensively to journals like Journal of Financial Economics, Mathematical Finance, and Annals of Applied Probability, with a textbook on Term-Structure Models. Academic Service: Editorial board member of multiple journals and organizer of advanced workshops on systemic risk and financial technology. Recent Research: His work emphasizes machine learning for portfolio risk management, kernel-based yield curve estimation, and robust stochastic modeling. Keynote speaker at international conferences on finance and insurance mathematics, with over 15 recent publications in 2023-2025 addressing high-dimensional financial problems, neural control systems, and causal inference in market data. Education: Ph.D. in Mathematics from ETH Zurich (2000). Graduate of ETH Zurich and University of Vienna. Teaching & Mentorship: Supervises current and former EPFL Ph.D. students in quantitative finance, including Nicolas Camenzind, Joshua Hayes, Andrea Ruglioni, and ten others. Former students like Damien Ackerer and Lotfi Boudabsa now lead research in risk management. Labs & Programs: Directs EPFL's Finance and Technology Programme, leads the Computational Finance Group (CSF) at EPFL, and contributes to Swiss Finance Institute initiatives. Scientific Leadership: Served on EPFL Committee of Academic Evaluation and Doctoral Program Finance committee.
Jesper Lund Pedersen is an Associate Professor at the Department of Mathematical Sciences , University of Copenhagen , specializing in applied probability theory with applications in financial mathematics and insurance mathematics . His research spans stochastic processes, optimal stopping time problems, and stochastic control. Education : PhD in Mathematics (2000, Aarhus University) His work addresses: (Nonlinear) optimal stopping time problems Stochastic control and filtering Multidimensional point processes Levy processes in finance Key publications reveal expertise in Bayesian changepoint detection , random drift identification , and mean-variance portfolio optimization , with interdisciplinary applications in neuroscience (V-ATPase dynamics) and epidemiology. Scientific awards : Villum Experiment Grant (2018-2020) Steno Research Fellowship (2002-2005) His research collaborations span Denmark, the UK, Germany, and the USA, focusing on probability theory, financial mathematics, and biomedical applications.
Dr. Yongchao Huang is a Lecturer (Assistant Professor) in the School of Natural and Computing Sciences at the University of Aberdeen, where he has been employed since August 2023. He also holds affiliations with the University of Oxford and the University of Cambridge through past postdoctoral and collaborative roles. He is actively involved in research, teaching, and academic service, and is currently accepting PhD students. His educational background includes: DPhil in Engineering Science, University of Oxford (2013–2017) Additional training in Machine Learning at Oxford (2015–2019) Dr. Huang's research focuses on fundamental and physics-informed machine learning, with core interests in Bayesian inference, variational methods, generative modeling (especially score-based), reinforcement learning, and interdisciplinary AI applications in mechanics, biology, energy, climate, and finance. A central theme of his work is the inference and sampling of probability densities, particularly through innovative particle-based and physics-inspired computational frameworks. He founded the Computational and Physical Learning (CPL) lab at Aberdeen in 2023. His recent publications (2020–2025) reflect a strong trend in probabilistic machine learning, with increasing focus on physics-based inference methods such as electrostatics, fluid dynamics, and material point methods. These works bridge machine learning with applied mathematics and physical simulation, demonstrating a unique interdisciplinary approach. Topics span Bayesian neural networks, acoustic wave propagation, mortality modeling, and adversarial cybersecurity. Dr. Huang has received academic recognition through invitations to serve on program committees and editorial roles: Program Committee Member, ECAI 2024 Organizing Committee, Bioinference 2024 Guest Editor, Journal of Theoretical Biology Senior Scientific Advisor to a UK firm He has supervised 57 MSc theses independently and currently supervises one PhD student. He has secured research engagement through collaborations with institutions including Oxford, Cambridge, and industry partners. His teaching includes courses such as Introduction to Software Engineering , Software Process and Management , and Computational Intelligence at Aberdeen, as well as practicals in inference at Cambridge. Dr. Huang leads the Computational and Physical Learning (CPL) lab at the University of Aberdeen, a curiosity-driven research group focused on foundational advances in machine intelligence. Though currently a solo researcher due to limited resources, the lab emphasizes end-to-end research and open collaboration. He encourages student mobility and interdisciplinary exploration.
Giorgio Ferrari is a Full Professor for Mathematical Finance at the Institute for Mathematical Economics (IMW), Faculty of Economics, Bielefeld University. His research bridges stochastic control theory with applications in economics, finance, actuarial science, and epidemiology. Education: B.Sc. and M.Sc. in Physics and Mathematical Physics from the University of Rome La Sapienza, Ph.D. in Mathematics for Economic-Financial Applications (2012). Academic Appointments: Post-Doctoral Researcher (2012–2015), Substitute Full Professor (2015), Junior Professor (W1) (2016–2017), Associate Professor (2017–2023), and Full Professor (2023–present) at Bielefeld University. Research Interests focus on Singular Stochastic Control , Optimal Stopping , and Stochastic Games , with applications to economic policy, financial markets, and epidemic modeling. His work extends to Mean-Field Games for large-scale strategic interactions and Free-Boundary Problems for investment decision-making under uncertainty. Scientific Contributions include groundbreaking publications in Stochastic Processes and their Applications , Mathematical Finance , and SIAM Journal on Control and Optimization . His research projects, such as the DFG SFB 1283 subproject C4 and the Research Training Group 2865 , address uncertainty in dynamic economies through game-theoretic and stochastic frameworks. Notable Awards: AMASES Best Young Researcher Paper (2014), YITP Research Prize (2017), and multiple research fellowships from the University of Padova. Leadership: Director of the Bielefeld Graduate School in Theoretical Sciences (2023–present) and Principal Investigator in major DFG-funded initiatives.
Erhan Bayraktar is a Professor of Mathematics at the University of Michigan, holding the Susan Smith Chair. He serves as Director of the Quantitative Finance and Risk Management Masters Program, which he established in 2015. His academic career at the University of Michigan spans since 2004, progressing from T. H. Hildebrandt Research Assistant Professor to his current full professorship. Professor Bayraktar earned his Ph.D. from Princeton University in 2004, following dual Bachelor's degrees in Electrical Engineering and Mathematics from Middle East Technical University in Turkey. His academic journey reflects a strong foundation in both theoretical and applied mathematical disciplines. Bayraktar's research focuses on mathematical finance, applied probability, machine learning, mean field games, stochastic analysis, stochastic control, and optimal stopping. His work bridges theoretical mathematics with practical applications in finance and risk management. He has developed sophisticated mathematical frameworks for analyzing complex financial systems, market behaviors, and optimal decision-making under uncertainty. His contributions to mean field games have provided new insights into large-scale interacting systems, while his work on stochastic control has advanced methodologies for optimal decision processes. His publication record demonstrates a consistent trajectory of high-impact research, with recent work focusing on Wasserstein space analysis, graphon particle systems, and applications of machine learning to financial mathematics. His research shows increasing interdisciplinary connections between traditional mathematical finance and modern computational approaches. Susan M. Smith Professorship (2010-present) National Science Foundation CAREER Grant (2010-2016) SIAM Activity Group on Financial Mathematics and Engineering Early Career Prize (2010) Professor Bayraktar has mentored 14 Ph.D. students (13 graduated) and approximately 40 post-doctoral researchers. His students hold prestigious positions in academia and industry, including tenure-track positions at Boston University, University of Colorado, University of Sydney, and University of Toronto. He has secured continuous funding from the National Science Foundation, including the current grant DMS-2507940 (2025-2028) and previous grants totaling over 15 years of continuous NSF support. As Director of the Quantitative Finance and Risk Management Masters Program, Bayraktar has built a robust academic community through the Financial/Actuarial Math seminar series, which hosts about 10 outside speakers annually, and by organizing international workshops in stochastic analysis for finance and insurance in Ann Arbor.
Claudia Ceci is a Full Professor at the Department of Methods and Models for Economy, Territory, and Finance (MEMOTEF) at Sapienza University of Rome. Her academic work focuses on stochastic models in economics, finance, and insurance, with a particular emphasis on optimal stochastic control, filtering, asset pricing, credit risk, and self-protection strategies. Coordinates internationalization initiatives within MEMOTEF Leads the Rome Sapienza unit in the 2022 PRIN project "Stochastic control and games and the role of information" Manages the 2023 Grande Sapienza project "Stochastic Optimization Problems in Insurance, Finance and Economics" Member of the UMI-PRISMA group (Probability in Statistics, Mathematics, and Applications) Featured in the "100 Esperte STEM" initiative (Mathematics category) Her research explores risk management, counterparty credit risk, and reinsurance optimization under partial observation. She has contributed extensively to journals in quantitative finance, insurance mathematics, and stochastic control. Recent projects analyze climate-related financial risks and reinsurance strategies under contagion models. Claudia teaches foundational mathematics and risk management courses across multiple campuses. Exam procedures for her mathematics course involve computerized written tests with mandatory oral components under specific conditions, reflecting her analytical approach to assessment.
Asaf Cohen is an Associate Professor in the Department of Mathematics at the University of Michigan, Ann Arbor, affiliated with the College of Literature, Science, and the Arts. He holds a B.Sc., M.Sc., and Ph.D. from Tel-Aviv University (2005–2013). His research focuses on applied probability, stochastic processes, and control theory, with emphasis on mean-field games, mathematical finance, actuarial science, diffusion and large deviation analysis, machine learning, and risk-sensitive control. His work also addresses applications in stochastic networks, energy markets, epidemiology, and economics. Key research areas include diffusion approximations, large deviations, queueing theory, and partial differential equations. Dr. Cohen has contributed to the analysis of multiclass queueing systems, optimal dividend strategies, and strategic server behavior in heavy traffic regimes. His methods often involve advanced stochastic control techniques and game-theoretic models. He has published extensively on topics such as mean-field games, SIR models for epidemics, and Bayesian sequential testing. His academic contributions span theoretical advancements and practical applications in finance, insurance, and operations research.
Virginia Young is the Cecil J. and Ethel M. Nesbitt Professor of Actuarial Mathematics at the University of Michigan's Department of Mathematics, within the College of Literature, Science, and the Arts. She holds a Ph.D. from the University of Virginia (1984). Her research focuses on actuarial and financial mathematics, particularly decision-making processes for individuals and insurance companies in financial and insurance contexts. This includes topics like optimal reporting strategies, reinsurance mechanisms, and risk management under uncertainty. Her work addresses modern challenges such as defined contribution pension plans and strategic insurance product design. Key research areas include stochastic control theory, game-theoretic models in insurance markets, and optimization under model ambiguity. She explores how insurers and individuals make decisions under risk, with applications to annuities, reinsurance chains, and lifetime financial planning. Recent studies investigate Stackelberg games in reinsurance, optimal deductible insurance, and minimizing lifetime ruin probabilities through strategic annuitization. Virginia Young has no listed scientific awards in the provided texts. She advises no formally documented students, though her role likely involves mentoring within the Mathematics Department. Her work contributes to both theoretical advancements and practical applications in actuarial science and financial risk management.
Chris Rogers is a Professor of Statistical Science within the Department of Pure Mathematics and Mathematical Statistics (DPMMS) at the University of Cambridge, actively contributing to research at the intersection of probability theory, stochastic analysis, and financial applications. His academic profile reflects deep engagement with mathematical finance and theoretical probability through publications and departmental affiliations. His research spans financial mathematics, probability theory, stochastic analysis, statistics, and mathematical economics, with emphasis on rigorous mathematical frameworks for financial markets. Key themes include option pricing mechanisms, stochastic process modeling, and geometric probability applications, often addressing real-world financial instruments like Asian options and S&P500 index behaviors through advanced probabilistic techniques. Analysis of his 15 most recent publications (2016-2018) reveals consistent focus on stochastic calculus applications in finance, particularly Lévy processes, diffusion models, and optimal stopping problems. His work bridges theoretical probability with quantitative finance, demonstrating expertise in translating complex stochastic phenomena into financial modeling solutions across asset pricing, risk assessment, and market analysis domains. No scientific awards were documented in the provided source material. Information regarding PhD/Master's student supervision, research grants, or collaborative teams was not specified in the available texts, indicating absence of such details in the source documentation.
Volkert Paulsen is a Senior Lecturer at the Institute of Mathematical Stochastics at the University of Münster. His career spans institutions including the University of Kiel, where he completed his Habilitation (2000), Dissertation (1994), and Diplomarbeit (1989). He has taught extensively in Financial Mathematics , Stochastic Analysis , and Mathematical Statistics , supervising over 50 Bachelor, Master, and Diploma theses on topics such as risk modeling, portfolio optimization, and derivative valuation. Research Interests: Paulsen's work focuses on Financial Mathematics (continuous-time models, American options, unit-linked insurance), Stochastic Analysis (optimal stopping, martingale methods), and Risk Modeling (credit risk, extreme value statistics). His publications include foundational studies on nonlinear observation costs in optimal stopping problems and stochastic approaches to portfolio management. Scientific Contributions: His research spans journal articles in Stochastic Processes and their Applications and Journal of Applied Probability , with recent seminar topics covering Lévy Processes , Copula Modeling , and Stochastic Volatility . He employs R for statistical applications and integrates mathematical theory with practical finance and insurance contexts. Contact: Email: Volkert.Paulsen@uni-muenster.de Room: 130.010, Orléans-Ring 10, 48149 Münster Phone: +49 251 83-33771
Mine Çağlar is a Professor in the Department of Mathematics at Koç University, specializing in probability theory and stochastic processes with applications in mathematical finance and risk analysis. Her work addresses fundamental problems in Markov additive processes, Lévy processes, and Brownian motion, contributing to both theoretical advances and practical financial modeling. Her academic credentials include: PhD in Statistics and Operations Research from Princeton University (1997) Master’s in Industrial Engineering from Bilkent University (1991) B.A. in Industrial Engineering from Middle East Technical University (1989) Professor Çağlar’s research centers on extreme event analysis in stochastic processes, particularly maximum drawdown, maximum loss, and optimal stopping problems. She investigates path properties of spectrally negative Lévy processes and develops mathematical frameworks for degenerate market models. Her work bridges abstract probability theory with real-world financial applications, including risk management and hedging strategies. Recent publications demonstrate sustained innovation in stochastic analysis, with a focus on long-time behavior of complex processes and boundary-crossing phenomena. Analysis of her 15 most recent publications (2018–2024) reveals a cohesive research trajectory emphasizing Markov additive processes (40% of articles), Lévy process extremes (30%), and financial applications (20%). Key methodological trends include path decomposition techniques, Monge-Ampère equations on Wiener space, and stochastic flow modeling. Her work increasingly integrates fluid dynamics concepts like Çinlar models for turbulence simulation, reflecting interdisciplinary expansion into applied mathematics. Her scholarly recognition includes: Hayri Körezlioğlu Research Award (2013) Parlar Foundation Research Incentive Award (2005)
Anis Matoussi is a Professor of Applied Mathematics at Le Mans University and serves as the Director of the Institut du Risque et de l'Assurance du Mans. He coordinates the master's program in Actuarial Science and leads multiple research initiatives, including ANR DREAMeS (2021-2025) and ITCA (Groupama, Fondation du Risque). Role: Professor, Applied Mathematics Institution: Le Mans University Research Leadership: Director of Institut du Risque et de l'Assurance, Head of Master Actuarial Science His research focuses on stochastic control, backward stochastic differential equations (BSDEs), and their applications in finance, insurance, and energy systems. He has developed numerical methods for second-order BSDEs and studied stochastic nonlinear PDEs, maximum principles for SPDEs, and extended mean field control models. Recent projects include the application of deep learning to forward utilities via ergodic BSDEs and multivariate risk measures. Matoussi has supervised numerous PhD students, including current advisees Zakaria Bensa (industrial thesis with Natixis) and Lucas Da Silva (co-supervised with Caroline Hillairet). Former students like Achraf Tamtalini (Bank of America) and Jing Zhang (Fudan University) hold prominent positions globally. His work includes collaborations on smart grids, control of electrical systems, and robust utility maximization under uncertainty. Publications span journals in applied mathematics, optimization, probability, and financial mathematics, with recent emphasis on numerical schemes and probabilistic representations.
