Following our Spring School and workshop on Themes across Control and Reinforcement Learning, of the research semester programme on Control Theory and Reinforcement Learning, we have a workshop on Modern Applications of Control Theory and Reinforcement Learning.
On 20 and 21 May 2025, we are organizing the workshop on Modern Applications of Control Theory and Reinforcement Learning.
Applications of control theory and reinforcement learning are increasingly diverse. This workshop aims to foster transfer of methods of control and RL across these upcoming domains, especially complex adaptive systems such as climate change, socio-economics, neuroscience, and similar.
Registrations will open shortly.
Elena Rovenskaya is the IIASA Advancing Systems Analysis (ASA) Program Director. She is also a research scholar at the Optimal Control Department of the Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russia (on-leave). Her scientific interests lie in the fields of optimization, decision science, and mathematical modeling of complex socio-environmental systems.
Dr. Rovenskaya graduated in 2003 from the Faculty of Physics, Lomonosov Moscow State University, Russia. She received her PhD in 2006 at the Faculty of Computational Mathematics and Cybernetics, Lomonosov Moscow State University, Russia. In her PhD dissertation, Dr. Rovenskaya developed a new numerical method for solving a broad class of non-convex optimization problems.
In 2005, Dr. Rovenskaya participated in the IIASA Young Scientists Summer Program (YSSP). She continued to collaborate with the former Dynamic Systems (DYN) Program from 2006 to 2010, and later, from 2011 to 2012 with its successor, the Advanced Systems Analysis (ASA) Program at IIASA. From 2013 to 2020, Dr. Rovenskaya served as ASA Program Director and from 2019 to 2020, she was also appointed in the capacity of Acting IIASA Evolution and Ecology Program Director.
Dr. Rovenskaya was appointed Advancing Systems Analysis (ASA) Program Director from January 2021 as the institute moved to a new program structure. Currently, the new ASA Program includes 85+ scientists and aims to identify, develop, and deploy new systems-analytical methods, tools, and data that address the most pressing global sustainability challenges with greater agility, and help find solutions to those challenges that are both realistic and appropriate.
Diederik M. Roijers is the academic liaison for AI research for the City of Amsterdam, and a member of the AI Research team. His team aims to improve the lives of the citizens of Amsterdam through AI research.
Next to his work at the City of Amsterdam, he is a senior researcher at the AI lab at the Vrije Universiteit Brussel (VUB), and currently supervising PhD students.
His main research interests are urban AI, reinforcement learning, planning, multi-agent systems, and multi-objective decision making. Click for more information of his tutorials or book for an introduction to multi-objective models and methods for multi-agent systems, RL and planning, or his publication page for information about his latest research. Other interests his are game theory, machine learning, robotics, e-tutoring systems, and education.
He obtained his PhD at the University of Amsterdam under the supervision of Shimon Whiteson and Frans Oliehoek. Click here for more information or his PhD thesis for further details. After his PhD, he worked on social robotics in the TERESA project, at the Department of Computer Science of the University of Oxford; as an FWO Postdoctoral Fellow on Multi-objective Reinforcement learning with Guarantees at the Vrije Universiteit Brussel; as assistant professor at the Vrije Universiteit Amsterdam; and as senior lecturer and researcher at the Institute of ICT and the Microsystems Technology Research Group where he worked on efficient AI for microproccessing systems and sensor data.
Herke van Hoof is associate professor at the University of Amsterdam. Before that, Herke van Hoof was a postdoc at McGill University in Montreal, Canada, where he worked with Professors Joelle Pineau, Dave Meger, and Gregory Dudek. He obtained his PhD at TU Darmstadt, Germany, under the supervision of Professor Jan Peters, where he graduated in November 2016. Herke got his bachelor and master degrees in Artificial Intelligence at the University of Groningen in the Netherlands. His group works on various aspects of modular reinforcement learning. To address the low data efficiency of reinforcement learning from scratch, we investigate topics like using (symbolic) prior knowledge, modularity, and transferring knowledge between tasks.
Marcel van Gerven is Professor of Artificial Cognitive Systems and Principal Investigator in the Department of Machine Learning and Neural Computing of the Donders Institute for Brain, Cognition and Behaviour. Prof. van Gerven is an expert in machine learning and neuromorphic computing. His work ranges from understanding the computational mechanisms of learning, inference and control in natural and artificial systems to the development of new AI technology with applications in e.g. neuroscience, neurotechnology, healthcare and smart industry. Prof. van Gerven is recipient of several grants at the intersection of AI and neuroscience, such as Dutch Vidi, Crossover, Perspective and Gravitation grants as well as EU HBP and FET grants. He also received the Radboud Science Award for his scientific work. Prof. van Gerven is cofounder of Radboud AI and directs one of the European ELLIS units as part of the European Excellence Network in Machine Learning. He also contributes to the Healthy Data program, which aims to make AI accessible in healthcare, and is director of an Innovation Centre in AI for semiconductor manufacturing. Through his work, he aims to bridge the gap between natural and artificial intelligence and contribute to the development of sustainable AI solutions that make a positive impact in science, industry and society.
