Control Theory and Reinforcement Learning: Connections and Challenges - workshops

Following our Spring School 2025 on Control Theory and Reinforcement Learning, we have a general workshop on “Themes across Control and Reinforcement Learning”.

When
24 Mar 2025 from 9 a.m. to 25 Mar 2025 6 p.m. CET (GMT+0100)
Where
Science Park 125, Turingzaal
Add

We invite contributions for talks and/or posters from practitioners in theory and applications in these fields. The organisers will allot talks based on topic and available slots. Attendance without presenting is welcome as well.

Speakers

Prof. Dr. Bert Kappen

Prof. Dr. Bert Kappen completed his PhD in theoretical particle physics in 1987 at the Rockefeller University in New York. From 1987 until 1989 he worked as a scientist at the Philips Research Laboratories in Eindhoven, the Netherlands. Since 2004 he is full professor on machine learning and neural networks at the science faculty of the Radboud University. In 1998, he co-founded the company Smart Research that commercializes applications of neural networks and machine learning.

Bert Kappen conducts research on neural networks, Bayesian machine learning, stochastic control theory and computational neuroscience. Currently, he is investigating ways to use quantum mechanics for a new generation of quantum machine learning algorithms and control methods for quantum computing.

Dr. Debabrota Basu works on designing theoretically-grounded and practically-efficient algorithms for bandits and Reinforcement Learning. He also conducts active research on robustness, privacy, and fairness in machine learning, in brief responsible AI. He actively works on developing robust, private, and fair Reinforcement Learning for algorithmic decision making in health, education, and agro-ecology. He was awarded ANR young researcher grant for studying impacts of responsible AI constraints in sequential decision making. In the ACM EAAMO 2022 conference, his work on fair college admissions got the best student paper award. In IJCAI 2023, he presented the tutorial on "Auditing Bias of Machine Learning Algorithms". Till now, he has delivered multiple talks to general audiences, policymakers, and lawmakers in Europe, Asia, and USA on frontiers and opportunities of algorithmic auditing. He has been elected as a scholar of European Learning Society (ELLIS) in 2024.

Dr. Frans A. Oliehoek is Associate Professor at Delft University of Technology, where he is a leader of the sequential decision making group, a scientific director of the Mercury machine learning lab, and director and co-founder of the ELLIS Unit Delft. He received his Ph.D. in Computer Science (2010) from the University of Amsterdam (UvA), and held positions at various universities including MIT, Maastricht University and the University of Liverpool. Frans' research interests revolve around intelligent systems that learn about their environment via interaction, building on techniques from machine learning, AI and game theory. He has served as PC/SPC/AC at top-tier venues in AI and machine learning, and currently serves as associate editor for JAIR and AIJ. He is a Senior Member of AAAI, and was awarded a number of personal research grants, including a prestigious ERC Starting Grant.

Prof. Dr. Sean Meyn was raised by the beach in Southern California. Following his BA in mathematics at UCLA, he moved on to pursue a PhD with Peter Caines at McGill University. After about 20 years as a professor of ECE at the University of Illinois, in 2012 he moved to beautiful Gainesville. He is now Professor and Robert C. Pittman Eminent Scholar Chair in the Department of Electrical and Computer Engineering at the University of Florida, and director of the Laboratory for Cognition and Control. He also holds an Inria International Chair to support research with colleagues in France. His interests span many aspects of stochastic control, stochastic processes, information theory, and optimization. For the past decade, his applied research has focused on engineering, markets, and policy in energy systems.

Registration details to follow.

Programme (To Be Determined).

More information

More information about the Research Semester Programme "Control Theory and Reinforcement Learning: Connections and Challenges" can be found here.