As a part of our semester programme, we organise a workshop on “Theory of Control and Reinforcement Learning” on June 19-20, 2025 at CWI, Amsterdam.
Register here for the workshop
We invite contributions for talks and/or posters from researchers in the theory of control and RL, especially ones bridging them. The organisers will select talks based on topic and available slots. For contributed talks, we will confirm the speakers by June 4th, 2025. Attendance without presenting is welcome as well.
Registration deadline: 4 June 2025.
Andreas Krause is a Professor of Computer Science at ETH Zurich, where he leads the Learning & Adaptive Systems Group. He also serves as Academic Co-Director of the Swiss Data Science Center and Chair of the ETH AI Center, and co-founded the ETH spin-off LatticeFlow. Before that he was an Assistant Professor of Computer Science at Caltech. His research focuses on learning and adaptive systems that actively acquire information, reason and reliably make decisions in complex and uncertain domains. His works advances principles of online, active and reinforcement learning, probabilistic and generative modeling for optimization and control, and engages them in real-world applications. He is a Max Planck Fellow at the Max Planck Institute for Intelligent Systems, ACM Fellow, IEEE Fellow, ELLIS Fellow, a Microsoft Research Faculty Fellow and a Kavli Frontiers Fellow of the US National Academy of Sciences. He received the Rössler Prize, ERC Starting and ERC Consolidator grants, the German Pattern Recognition Award, an NSF CAREER award as well as the ETH Golden Owl teaching award. His research on machine learning and adaptive systems has received awards at several premier conferences and journals, including the ACM SIGKDD Test of Time award 2019 and the ICML Test of Time award 2020. From 2023-24, he served on the United Nations’ High-level Advisory Body on AI.
Anuradha Annaswamy is the Founder and Director of the Active-Adaptive Control Laboratory in the Department of Mechanical Engineering at MIT. She previously held faculty positions at Yale and Boston University, and is currently a Senior Research Scientist at MIT. Her research interests span adaptive control theory and its applications to aerospace, automotive, propulsion, and energy systems as well as cyber-physical systems such as Smart Grids, Smart Cities, and Smart Infrastructures. She has received best paper awards (Axelby, 1986; CSM, 2010; IFAC Annual Reviews in Control, 2021-23), Distinguished Member and Distinguished Lecturer awards from the IEEE Control Systems Society (CSS), and a Presidential Young Investigator award from NSF, 1991-97. She is a Fellow of IEEE and the International Federation of Automatic Control and recipient of the Distinguished Alumni Award from Indian Institute of Science for 2021. She received the IEEE Control Systems Technology Award from CSS in 2024.
Anu Annaswamy is the author of a graduate textbook on adaptive control, co-editor of two vision documents on smart grids and two editions of the Impact of Control Technology report, and editor of IEEE Open Journal of Control Systems, the IFAC Annual Reviews in Control, and Asian Journal of Control. She has co-authored two National Academies consensus reports: The Future of Electric Power in the United States (2021) and The Role of Net Metering in the Evolving Electricity System (2023). She served as the President of CSS in 2020. Currently, Dr. Annaswamy serves as President-elect of the American Automatic Control Council and as Editor in Chief of the IEEE Control Systems magazine. Dr. Annaswamy received her Ph.D. in Electrical Engineering from Yale University in 1985.
Emilie Kaufmann is a CNRS researcher in the CRIStAL laboratory at Université de Lille. She is also a member of the Inria team Scool. She is interested in statistics and machine learning, with a particular focus on sequential learning. She has studied variants of the Multi-Armed Bandit (MAB) and Markov Decision Processes (MDPs) under both reinforcement learning ("maximize rewards while learning") and adaptive testing ("learn as fast as you can by adaptively collecting data") formulations. On the application side, her recent interest is in the potential use of bandit strategies for adaptive early stage clinical trials, and in the use of contextual bandits for precision medicine. She won the 2014 Jacques Neveu Prize for the best PhD thesis in mathematics and statistics in France, and CNRS bronze medal in 2024.
Gergely Neu is a Research Assistant Professor at the AI group of Universitat Pompeu Fabra. He is a machine learning researcher mainly interested in theoretical aspects of sequential decision making. He mainly works on online optimization, bandit problems, and reinforcement learning theory. He likes to think about algorithms that come with performance guarantees both in terms of computational and statistical complexity, and are actually implementable on a computer. He has received an ERC Starting Grant in 2020, the first Bosch AI Young Researcher Award in 2019, and a Google Faculty Research Award in 2019.
Jan Peters is a full professor for Intelligent Autonomous Systems at the Computer Science Department of the Technische Universität Darmstadt and, at the same time, he is the dept head of the research department on Systems AI for Robot Learning (SAIROL) at the German Research Center for Artificial Intelligence (Deutsches Forschungszentrum für Künstliche Intelligenz, DFKI). He is also is a founding research faculty member of The Hessian Center for Artificial Intelligence. Creating autonomous robots that can learn to assist humans in situations of daily life is a fascinating challenge for machine learning. The goal of his robot learning laboratory is the investigation of the ingredients for such a general approach to motor skill learning, getting closer to human-like performance in robotics and to achieve the first step of creating robots that can learn to accomplish many different tasks triggered by environmental context or higher-level instruction. Jan has received a few awards, most notably, he has received the Dick Volz Best US PhD Thesis Runner Up Award, the Robotics: Science & Systems - Early Career Spotlight, the IEEE Robotics & Automation Society's Early Career Award, and the International Neural Networks Society's Young Investigator Award. He has also been a co-founder of the IEEE Robotics and Automation Society's Technical Committee on Robot Learning which won the Most Active Technical Committee Award.
Michael Muehlebach is leading the independent research group Learning and Dynamical Systems at the Max Planck Institute for Intelligent Systems in Tuebingen, Germany. Michael did his PhD from Institute for Dynamic Systems and Control at ETH Zurich and postdoc from University of California, Berkeley. He is interested in a wide variety of subjects, including machine learning, dynamics, control, and optimization. During my Ph.D. He worked on approximations of the constrained linear quadratic regulator problem with applications to model predictive control. He also analyzed first-order optimization algorithms from a dynamical system's point of view. He is a Branco Weiss Fellow since 2018, was awarded the Emmy Noether Fellowship in 2020, and an Amazon Fellowship in 2024.
Peter Grünwald is senior researcher in the machine learning group at CWI in Amsterdam, the Netherlands. He is also full professor of Statistical Learning at the mathematical institute of Leiden University. Peter is the recipient of a prestigious ERC Advanced Grant (2024) (CWI announcement and interviews (in Dutch) in national newspapers De Volkskrant and Trouw– see here for more general biographical information and recent media exposure). The project, and Peter’s research in general, is about creating a much more flexible theory of statistical inference, based on the emerging theory of e-values and e-processes. E-values (wikipedia) are an alternative to p-values that effortlessly deal with optional continuation: with e-value based tests and the corresponding always valid confidence intervals, one can always gather additional data, while keeping statistically valid conclusions. From 2018-2022, Peter served as the President of the Association for Computational Learning, the organization running COLT, the world’s prime annual conference on machine learning theory. He is editor of Foundation and Trends in Machine learning, and author of the book "The Minimum Description Length Principle" (MIT Press, 2007). He is co-recipient of the Van Dantzig prize in 2010, the highest Dutch award in statistics and operations research.
Roxana Rădulescu is currently an Assistant Professor in AI and Data Science, at the Department of Information and Computing Sciences, at Utrecht University. Before this, she was a FWO Postdoctoral fellow at the Artificial Intelligence Lab, Vrije Universiteit Brussel, Belgium. Her research is focussed on the development of multi-agent decision making systems where each agent is driven by different objectives and goals, under the paradigm of multi-objective multi-agent reinforcement learning.
Tim van Erven is an associate professor at the Korteweg-de Vries Institute for Mathematics at the University of Amsterdam in the Netherlands. His research explores the mathematical foundations of machine learning. His research group designs mathematically well-founded machine learning methods for online convex optimization that work well out of the box, without any manual fine-tuning. In 2023, he has joined the board of directors of COLT association. He has been awarded the VICI grant by the Dutch Research Councilin 2019 and 2025.
09:00 - 09:30 Registration and tea/coffee
09:30 - 09:45 Welcome speech by Peter Grünwald
09:45 - 10:45 Emilie Kaufmann
10:45 - 11:15 break
11:15 - 12:15 Gergely Neu
12:15 - 13:30 Lunch break
13:30 - 15:00 Contributed talks
15:00 - 15:30 break
15:30 - 16:30 Roxana Rădulescu
16:30 - 17:30 Andreas Krause
17:30 - 19:00 Posters and Discussion with drinks/bites
09:00 - 09:30 Registration and tea/coffee
09:30 - 10:30 Anuradha Annaswamy
10:30 - 11:00 break
11:00 - 12:00 Tim van Erven
12:00 - 13:30 Lunch and Poster session
13:30 - 14:30 Michael Muehlebach
14:30 - 15:30 Jan Peters
15:30 - 16:00 break
16:30 - 17:30 Panel discussion
17:30 - 18:00 Posters and discussion with tea/coffee
The conference will be held in the Turing room at the Congress Centre of Amsterdam Science Park, next to Centrum Wiskunde & Informatica (CWI).
Address: Science Park 125, 1098 XG Amsterdam.
Please be aware that hotel prices in Amsterdam can be quite steep. We strongly recommend all participants to secure their hotel reservations as early as possible!
Hotel Recommendations
* Generator Hostel
* MEININGER Hotel (Amsterdam Amstel)
* Hotel Casa
* The Manor Amsterdam
* The Lancaster Hotel Amsterdam
From these hotels, the venue can be reached in 15-30 minutes with public transport. In all public transportation, you can check in and out with a Mastercard or Visa contactless credit card and also with Apple Pay and Google Wallet.
Sharing a hotel room is a great way to reduce costs! If you are attending and are interested in room-sharing arrangements, please send an email to events@cwi.nl .
