MSCA TRAINING - Reinforcement Learning @UTW

Register on Meetup: MSCA Training Week - UT

The 4EC doctoral training course "Reinforcement Learning in Digital Finance" offers a comprehensive exploration of reinforcement learning (RL), which is a powerful framework to support sequential decision-making under uncertainty.

Candidates will delve into the theoretical foundations of reinforcement learning, state-of-the-art algorithms, and practical applications within the digital finance domain. RL can facilitate and enhance sequential decision-making processes and services in finance, such as algorithmic trading, portfolio optimization, risk management, and fraud detection. Doctoral candidates will gain a solid understanding of the theoretical underpinnings of RL as well as practical experience in implementing reinforcement learning algorithms to solve real-world financial problems. The applications will consider constraints and regulations that concern privacy, algorithmic bias, and explainability.

By the end of the course, candidates will be equipped with the knowledge and skills to leverage reinforcement learning for optimizing control, enhancing performance, and improving services in the dynamic landscape of digital finance.

Speakers

Wouter van Heeswijk is an assistant professor in operations research & financial engineering at the University of Twente. His research efforts focus primarily on reinforcement learning, with both methodological developments and applications across domains. He teaches a number of courses in financial engineering, with topics including reinforcement learning in finance, numerical valuation of derivatives, real option analysis, risk management and financial accounting. Within the DIGITAL network, he is primarily involved in the doctoral training programme.

Martijn Mes is a full professor of Transportation and Logistics Management (TLM) and chair of the Industrial Engineering and Business Information Systems (IEBIS) section within the High Tech Business and Entrepreneurship (HBE) department at the University of Twente (Enschede, The Netherlands). He holds a master’s degree in Applied Mathematics (2002) and did his PhD at the School of Management and Governance, University of Twente (2008). After finishing his PhD, Martijn did his postdoc at Princeton University, Department of Operations Research and Financial Engineering, where he did research on the topics of Ranking and Selection (R&S), Bayesian Global Optimization (BGO), and Optimal Learning.

Joerg Osterrieder is Associate Professor of Finance and Artificial Intelligence at the University of Twente in the Netherlands, Professor of Sustainable Finance at Bern Business School in Switzerland, and Advisor on Artificial Intelligence to the ING Group's Global Data Analytics Team. He has more than 15 years of experience in financial statistics, quantitative finance, algorithmic trading, and the digitization of the finance industry. Joerg is the Chair of the European COST Action 19130 Fintech and Artificial Intelligence in Finance, an interdisciplinary research network comprised of over 300 researchers from 51 countries globally.

Branka Hadji Misheva is a Professor in Applied Data Science and Finance at BFH, working on AI applications in finance, XAI methods, network models and fintech risk management. She holds a PhD in Economics and Management of Technology with a specific focus on network models as they apply to the operation and performance of P2P lending platforms, from the University of Pavia, Italy. She has furthermore participated in the acquisition of over 20 SNF, Innosuisse and EU projects and published a variety of papers related within the different research proposals Prof. Hadji Misheva is also research author of over 25 papers in the field of credit risk modeling, graph theory, predictive performance of scoring models, lead behavior in crypto markets and explainable AI models for credit risk management.

Stefano Penazzi is a key member of the Cardo AI team, serving as a Senior Data Scientist. Stefano contributes to the company's mission by leveraging his extensive expertise in data science in developing predictive models, which in turn is shaped by multiple years spent in research positions across distinguished European academic institutions (University of Bologna, University of Hull and ETH Zurich). Prior to Cardo AI, Stefano has also contributed to the development of a decision support system in Python, employed by the Australian Government, to assess and forecast the economic, environmental, and social impacts resulting from the introduction of biofuel.

Anne Zander is an Assistant Professor in Applied Mathematics at the University of Twente, The Netherlands. She studied Mathematics and Physics and earned a Ph.D. in 2021 from the Karlsruhe Institute of Technology, Germany, working on Operations Research methods applied to Healthcare Logistics. Her theoretical research focuses on sequential decision problems, particularly high-dimensional, discrete decision spaces. To solve those problems, she aims to integrate lookahead planning via Stochastic Programming and learning from past experience via Reinforcement Learning. Her main field of application is Healthcare Logistics. Currently, Anne Zander participates in and leads several projects in close collaboration with healthcare providers related to capacity planning during infectious outbreaks or improving the patient flow from hospitals to follow-up care.

Warren B Powell is Professor Emeritus at Princeton University, where he taught for 39 years, and is currently the Chief Innovation Officer at Optimal Dynamics.   He was the founder and director of CASTLE Lab, which focused on stochastic optimization with applications to freight transportation, energy systems,  health, e-commerce, finance and the laboratory sciences, supported by over $50 million in funding from government and industry.  He has pioneered a new universal framework that can be used to model any sequential decision problem, including the identification of four classes of policies that spans every possible method for making decisions. This is documented in his latest book with John Wiley: Reinforcement Learning and Stochastic Optimization: A unified framework for sequential decisions.