University of Twente Hosts an MSCA Digital Finance PhD Training Week on Reinforcement Learning
Enschede, Netherlands— The University of Twente, in collaboration with several European institutions, within the framework of the MSCA DIGITAL project, will host several PhD training weeks and courses. This training program is designed for doctoral candidates seeking to explore the dynamic intersection of artificial intelligence and finance. For its first occurrence, the Twente Training week will be focusing on "Reinforcement Learning in Digital Finance."
Bringing Together a Global Teaching Team
The course is led by a number of academics and industry experts, including:
- Wouter van Heeswijk, Assistant Professor in Operations Research & Financial Engineering and this event’s coordinator, University of Twente.
- Jörg Osterrieder, Coordinator MSCA Digital Finance, Associate Professor in AI and Finance at the University of Twente
- Anne Zander, Assistant Professor in Stochastic Optimization, University of Twente.
- Martijn Mes, Full Professor in Transportation and Logistics Management, University of Twente.
- Branka Hadji Misheva, Professor in Applied Data Science and Finance, Bern University of Applied Sciences.
- Stefano Penazzi, Senior Data Scientist at Cardo AI.
- Warren Powell, Professor Emeritus at Princeton University and Chief Analytics Officer at Optimal Dynamics.
- Christian Spethmann, Data Scientist at Swedbank Group.
- Adrian Costea, Professor in Data Mining and Econometrics at Bucharest University of Economic Studies and Regional Director at the National Bank of Romania.
A Comprehensive Learning Experience
The course provides a robust introduction to reinforcement learning, with a strong emphasis on its applications in digital finance. Participants will learn bout the foundations of reinforcement learning, its applicability in the financial sector, and the explainability of AI-based decision making.
Key features of the program include:
- Lectures and Tutorials: Covering foundational and advanced topics such as Markov Decision Processes, policy-based reinforcement learning, deep reinforcement learning in finance, explainable reinforcement learning, and transfer learning.
- Guest Lectures: Featuring keynote speaker Prof. Dr. Warren Powell, professor emeritus at Princeton University and one of the pioneers in the field, who will provide insights into practical applications of reinforcement learning in finance.
- Hands-On Projects: Students will work on coding real-world financial problems and solving them with state-of-the-art reinforcement learning techniques, culminating in a final presentation and a research paper.
Meeting the Needs of Modern Finance
The course will equip doctoral candidates with the skills necessary to model, solve, and optimize complex financial problems. By the end of the program, participants will be proficient in:
- Applying reinforcement learning algorithms to digital finance problems.
- Developing algorithmic pipelines that stretch from data ingestion to explainable decision-making policies.
- Presenting findings and design choices to both technical and non-technical audiences.
Course Setup and Assessment
The program begins on February 3, 2025, with a blend of hybrid and online sessions. It concludes with final presentations in June 2025. Assessment is based on group projects, with a focus on code quality, experimental results, and comprehensive reports. Exceptional projects may even evolve into academic publications.
Enrollment and Requirements
Participants should have a foundational knowledge of stochastic models, Markov Decision Processes, Python programming, and statistics. While prior expertise is beneficial, motivated candidates are encouraged to apply, as supplementary resources and support are available.
To enroll, make sure to visit the event page and register your interest in our registration form
Feel free to show your interest in LinkedIn and share the event post.
Best regards
Wouter van Heeswijk
Jörg Osterrieder
Frédérik Bernard
Martijn Mes