Speakers

Christopher Blier-Wong is an Assistant Professor of Actuarial Science at the University of Toronto. His research intersects actuarial science, statistics, and machine learning, focusing on dependence modelling, machine learning with unstructured data, and e-variables. He has contributed to these fields through numerous publications in prestigious journals across actuarial science, statistics, machine learning, and applied probability. His scholarly work covers a broad spectrum of topics, including non-life insurance, risk aggregation, and applying machine learning techniques in insurance.

Tolulope Fadina is an Assistant Professor in Actuarial Science and Risk Management at the University of Illinois at Urbana-Champaign. Her research focuses on the application of model uncertainty to insurance, finance, and risk management. In 2022, she was named one of the Black Heroes of Mathematics by the International Center for Mathematical Sciences. Recently, she was awarded the University of Illinois Campus Research Board Award.

I am an Assistant Professor of Financial Engineering in the Department of Decision Sciences at HEC Montréal (business school of University of Montreal). Before joining HEC Montréal, I was a postdoctoral fellow in the Department of Statistical Sciences at the University of Toronto, Canada, between 2018-2020, where I worked with Sebastian Jaimungal in the mathematical finance program. I was also a PhD exchange student in the same program during Fall 2017. I completed my PhD in electrical engineering under the supervision of Peter E. Caines in the systems & control program at McGill University, Canada, in 2019.

My research interests include theoretical developments of stochastic control and mean field games, as well as their applications in financial markets. Specifically, I am interested in modeling energy and financial markets as large-population dynamic games, addressing issues such as optimal trading, systemic risk, equilibrium pricing, and contract design.

Samuel is an Assistant Professor in the Department of Mathematics at Toronto Metropolitan University. Samuel’s current research focuses on applying epidemiological models and network theory to understand the dynamics of systemic risk, bank runs, and financial instability, bank runs. Through this work, he aims to contribute to more resilient financial systems.

Klaus Herrmann is an assistant professor at the Department of mathematics, Faculty of science of the Université de Sherbrooke in Sherbrooke, Canada. My research interests are centered around dependence concepts in statistics and probability theory. Specific topics include copula-induced dependence structures, the aggregation of dependent random variables, extreme value theory and multivariate risk measures, with application to portfolio selection and quantitative risk management.

Anran Hu works at the intersection of stochastic control, game theory, optimization and machine learning. Her primary research areas are mean-field games, continuous-time stochastic control and reinforcement learning. She is also interested in FinTech and applying machine learning and reinforcement learning to finance.

A main focus of Hu's research is on the reinforcement learning of large-population games and continuous-time stochastic control systems. Another important thread of Hu's work is on developing optimization formulations and novel modeling generalizations of mean-field games and their extensions. Her research spans the full spectrum from fundamentally new frameworks, analytical theory, efficient algorithms to open-source software for such important problems which have wide applications in finance, economy, transportation, robotics and energy systems.

Before coming to Columbia University, Hu was a Hooke Research Fellow in the Mathematical Institute at the University of Oxford. She completed her Ph.D. in Berkeley IEOR, advised by Prof. Xin Guo. Before coming to Berkeley, she obtained my B.S. degree from the School of Mathematical Sciences, Peking University.

I joined The Ohio State University as an Assistant Professor in September 2024. I obtained my Ph.D. degree in Actuarial Science from the University of Waterloo in 2024, under the supervision of Dr. Pengyu Wei and Dr. Chengguo Weng. Prior to my doctoral studies, I completed my master's degree at the Hong Kong University of Science and Technology in 2020 and my bachelor's degree at the Southern University of Science and Technology in 2018. I have interdisciplinary research experience in exploring statistical optimization to design robust investment strategies and in developing efficient quantitative methods for risk evaluation. I also have programming experience in applying machine learning models to diverse domains, such as actuarial science, finance, and marketing. My research interests encompass actuarial science, quantitative finance, quantitative risk management, socially responsible investing, and the broader applications of machine learning.

Emma Hubert is a tenure-track Assistant Professor in the Operations Research and Financial Engineering (ORFE) department at Princeton University, a role she has held since September 2021. Her research focuses on stochastic control and games, particularly continuous-time Principal-agent problems and mean-field games, with applications to economics, finance, epidemics, and energy systems. Her work is partially supported by the NSF grant DMS-2307736. Emma holds a PhD in mathematics from Université Paris-Est, supervised by Professors Romuald Elie and Dylan Possamaï. She defended her thesis in December 2020, which was recognised with the Prix de thèse SMAI-GAMNI 2021, the Prix de thèse Paris-Est Sup 2021, and the Prix Paul Caseau 2021. Before joining Princeton, she completed a one-year postdoctoral fellowship at Imperial College London.

Thibaut Mastrolia earned his PhD in Applied Mathematics from Université Paris-Dauphine in 2015 and completed his habilitation thesis in Mathematics at Université Paris-Saclay in 2021. From 2016 to 2021, he served as an assistant professor at École Polytechnique, specializing in probability and finance. His research focuses on the intersection of stochastic analysis and optimal control, including stochastic differential games, mean field games, and the design and regulation of financial markets. He has published  in leading journals such as Mathematical Finance, Operations Research, SIAM Journal of Control and Optimization, and the Annals of Probability, among others. He has been awarded by the France-Berkeley fund for his project in Cyber Risk Modeling and Insurance in 2023.

In 2019 I received a PhD in Statistics from the University of Warwick. PhD thesis focused on optimal stopping problems and pricing of American options, and was awarded the Bruti Liberati prize by the Bachelier Finance society. Between 2019-2023 I was a non-tenure track Assistant Professor in the group of Prof. Erhan Bayraktar at the University of Michigan. There I worked on semi-martingale optimal transport problems with applications to mathematical finance. Since 2023, I have been a tenure-track Assistant Professor in the Department of Mathematics, North Carolina State University. My research spans several areas of probability theory with special interests in optimal transport, stochastic control, optimal stopping, and data-driven pricing and hedging of exotic derivatives.

Boston University

Siyang Tao, Ph.D., ASA, is an Assistant Professor in the Department of Mathematical Sciences at Ball State University, Indiana, USA. He earned his Ph.D. in Statistics with a concentration in actuarial science/financial mathematics from the University of Iowa in 2020, where he also completed a Master’s in Statistics in 2016. Prior to that, he earned a Master’s in Probability and Random Models from Pierre and Marie Curie University (Paris VI)/Sorbonne University in 2013 and dual Bachelor’s degrees in Mathematics from the University of Picardie Jules Verne and Huazhong University of Science and Technology in 2010. Dr. Tao’s research spans a variety of areas, with a primary focus on extreme value theory and quantitative risk management. His doctoral work centered on the sub-area of tail dependence within extreme value theory, particularly the Tail Dependence Matrix (TDM) realization problem, integrating concepts from probability theory, combinatorics, computational complexity, graph theory, and optimization. This research is vital for understanding correlated risks, such as stock market crashes or natural disaster claims, as highlighted by the 2008 financial crisis. Since joining Ball State in 2020, Dr. Tao has expanded his research to include credibility theory and the application of neural network architectures to address actuarial challenges. He also continues to advance his work on extreme value theory, particularly exploring the subsets of TDMs generated by commonly used copula families. His contributions have been published in prestigious journals, including ASTIN Bulletin, Extremes, Journal of Multivariate Analysis, and North American Actuarial Journal. He is also actively exploring new areas of research. In addition to his academic achievements, Dr. Tao earned the Associate of the Society of Actuaries designation in 2022 and is diligently pursuing the Fellowship of the Society of Actuaries.

University of Iowa

Qiuqi is an Assistant Professor at Maurice R. Greenberg School of Risk Science of Georgia State University. Prior to that, he obtained his PhD in Actuarial Science at the University of Waterloo in 2023. His research interests involve various topics in quantitative risk management including the characterization and optimization of risk measures and non-parametric backtesting approaches for risk measures. He won the Pierre Robillard Award from the Statistical Society of Canada for 2024.

I am currently an Assistant Professor in the Department of Mathematics at Carnegie Mellon University. From 2020-2023, I was an Assistant Professor in the Department of Statistics at Columbia University. In summer 2020, I received  a PhD from Oxford University under the supervision of Jan Obloj. For more information please see my CV.  My research focuses on mathematical finance and mathematical statistics with a special emphasis on optimal transport of stochastic processes. I am particularly interested in the robust approach to mathematical finance, which does not start with an a priori model but rather with the information available in the markets. My goal is to establish new connections to the theory of optimal transport on the one hand and robust statistics as well as machine learning on the other, in order to develop a universal toolbox for the implementation of robust and time-consistent trading strategies and risk assessment.

Dr. Linfeng Zhang is an assistant professor in the Department of Mathematics at the Ohio State University. He earned his Ph.D. degree in Mathematics with a concentration in Actuarial Science and Risk Analytics at the University of Illinois at Urbana-Champaign. He worked as a research assistant at the Critical Infrastructure Resilience Institute (CIRI) on topics related to cyber risk and cyber insurance from 2016 to 2019. He also worked on projects about cyber and privacy risks funded by the SOA, the Fundación MAPFRE, and Cisco. Linfeng published peer-reviewed academic papers about cyber risk in actuarial science, engineering, and law review journals, such as Geneva Papers on Risk and Insurance: Issues and Practice, IEEE Transactions on Emerging Topics in Computing, and Connecticut Insurance Law Journal.