Jón Atli Benediktsson
University of Iceland

Title: Large-Scale AI and Remote Sensing with Supercomputing used to Advance Geoscience

Abstract: The rapid proliferation of data in the new information era has increased the complexity of data-driven
problems across various fields of science and engineering. This development has led to a paradigm shift in AI, moving towards unsupervised and self-supervised representation learning, as well as multimodal learning. Significant advancements have emerged not only in mainstream Natural Language Processing and Computer Vision but also in Earth observation applications. These advancements exploit the synergies between self-supervised learning and the expanded availability of High-Performance Computing (HPC) systems, resulting in the emergence of AI Foundation Models (FMs). Originating from the concept of building upon an existing ‘foundation’, these models are developed by training on large and diverse data sets. This training enables them to capture a broad spectrum of informative features, making them extremely versatile and applicable across multiple domains. This keynote will provide an overview of the current efforts toward FMs for Earth observation at the ‘Remote Sensing Simulation and Data Lab’ of the Icelandic HPC community, University of Iceland, which collaborates closely with the Jülich Supercomputing Centre at the Forschungszentrum Jülich in Germany. The presentation will highlight the necessary tools and efforts required for the development of FMs, showcasing interdisciplinary research that intersects AI, supercomputing, and remote sensing applications. This research aims to enhance our understanding of complex Earth processes and advance the development of Digital Twins of Earth.

CV: Jón Atli Benediktsson received the Cand.Sci. degree in electrical engineering from the University of Iceland, Reykjavik, in 1984, and the M.S.E.E. and Ph.D. degrees in electrical engineering from Purdue University, West Lafayette, IN, in 1987 and 1990, respectively. Since 1991, Prof. Benediktsson has been with the University of Iceland, where he has been Professor of Electrical and Computer Engineering since 1996. He served as President and Rector of the University of Iceland from 2015 to 2025 and as President of Aurora Universities from 2020 to 2024. Prof. Benediktsson has been named to the Clarivate Highly Cited Researchers list since 2018. His research interests include remote sensing, biomedical signal analysis, pattern recognition, image processing, and signal processing, with extensive publications in these areas.

Prof. Benediktsson was the 2011–2012 IEEE Geoscience and Remote Sensing Society (GRSS) President and the 2003–2008 Editor-in-Chief of IEEE Transactions on Geoscience and Remote Sensing. Currently, he is Editor-in-Chief of IEEE Press and Senior Editor for the Proceedings of the IEEE. Prof. Benediktsson is an International Member of the National Academy of Engineering, a Life Fellow of IEEE, a Fellow of SPIE, and a member of Academia Europaea, Sigma Xi, and Tau Beta Pi. He has received the IEEE Third Millennium Medal (2000), the IEEE GRSS Outstanding Service Award (2007), the GRSS David Landgrebe Award (2018), and the IEEE GRSS Education Award (2020). Prof. Benediktsson was a co-recipient of the 2012 IEEE Transactions on Geoscience and Remote Sensing Paper Award. In 2013 and 2024, he was a co-recipient of the IEEE GRSS Highest Impact Paper Award.

Mustafa Khammash
ETH Zürich

Title: Cybergenetics: Toward a Control Theory of Living Systems

Abstract: Living cells are complex, stochastic dynamical systems that achieve remarkable robustness and adaptability through feedback. Cybergenetics seeks to bring the principles of control theory into this domain, enabling the real-time regulation of cellular behavior while also revealing new challenges and opportunities for control.

In this lecture, I will show how fundamental ideas from feedback control can be translated into genetic controllers that operate reliably within the noisy and nonlinear environment of living cells. I will present a universal internal model principle for biomolecular systems, showing that integral feedback and its generalizations underpin kinetics-independent robust tracking and disturbance rejection at the molecular scale. I will then show how the constraints of biological implementation, such as stochasticity and molecular discreteness, challenge classical assumptions and point toward new directions for control theory. These ideas will be illustrated through experimentally implemented genetic feedback controllers and emerging applications, including engineered cells that autonomously sense and respond to disease. Together, they establish a two-way bridge between control theory and biology, where feedback not only enables new capabilities in living systems but also expands the foundations of control toward a control theory of living systems.

CV: Prof. Mustafa Khammash is Professor of Control Theory and Systems Biology at ETH Zurich. Trained as an electrical engineer (B.S., Texas A&M University; Ph.D., Rice University), he previously held faculty positions at Iowa State University and University of California, Santa Barbara, where he directed the Center for Control, Dynamical Systems, and Computation. Since joining ETH Zurich in 2011, he has served as Vice Chair and Head of the Department of Biosystems Science and Engineering.

Khammash is a leading figure at the interface of control theory and biology. His work has played a central role in shaping the emerging field of cybergenetic control—the use of feedback to regulate living cells in real time—bringing core principles of control theory into biology. His contributions span robust control, stochastic dynamics, and systems and synthetic biology, and have enabled the design of adaptive and programmable cellular behaviors. His recent work advances toward therapeutic applications, including engineered cell-based systems that autonomously sense and respond to disease. He is a Fellow of IEEE, IFAC, JSPS and a recipient of multiple Advanced Grants from the European Research Council and the Swiss National Science Foundation.

Sergio Grammatico 

TU Delft

Title: Systems and Control Theory for Game Equilibrium Seeking

Abstract: Distributed game theory and optimal control provide the foundation for the analysis and design of multi-agent systems. A central challenge in this area is game equilibrium seeking, which this talk presents from a systems and control perspective. First, variational analysis, operator theory, and systems theory areemployed to model and analyze equilibrium-seeking algorithms as dynamical systems, thereby leading to a unified framework for their convergence. Dynamic games for constrained systems are considered next. Leveraging optimal control theory, equilibrium control policies are devised in feedback form associated with lifted optimal value functions, thus enabling receding-horizon model-predictive control in dynamic games. The talk concludes with an outlook on data-driven game equilibrium seeking for systems with partially known objective functions, dynamics, and constraints.

CV: Sergio Grammatico is an associate professor at the Delft Center for Systems and Control, TU Delft. He received his PhD from the University of Pisa (2013) and held research positions at ETH Zurich (2013–2015) and TU Eindhoven (2015–2017). His research interests revolve around game theoretic control and optimization for complex systems, for which he was awarded an ERC Starting Grant (2018) and an ERC Consolidator Grant (2025). Dr. Grammatico is a recipient of the Best Paper Award at the 2016 International Conference on Network Games, Control and Optimization, of the 2021 Roberto Tempo Best Paper Award, of the 2025 IEEE Transactions on Control of Network Systems Best Paper Award, and a co-author of the 2022 IEEE CSS Italy Young Author Best Journal Paper Award. He is an IEEE SYSC Distinguished Lecturer and serves as an associate editor of IEEE Transactions on Automatic Control and Automatica.

Line Roald
University of Wisconsin—Madison

Title: Carbon-Aware Load Control and the Challenges of Carbon Signals

Abstract: Demand response programs have traditionally focused on controlling electric loads to follow price signals, provide ancillary services to the grid, or reduce demand during periods of scarcity. While these programs are essential for operating power systems with high shares of renewable energy, it is difficult to explicitly link participation in demand response to measurable reductions in carbon emissions. At the same time, a growing group of electricity consumers—from hyperscale computing companies to individual households—can be described as “carbon-sensitive.” These consumers are willing to adapt their real-time electricity use based on the carbon intensity of the grid, much like price-sensitive consumers respond to fluctuations in electricity prices. We refer to this practice as carbon-aware load control. In this talk, we examine the carbon intensity signals commonly used to guide carbon-aware load control and demonstrate that they can sometimes lead to counterintuitive or even counterproductive impacts on overall grid emissions. We then propose an approach for integrating consumer carbon preferences into electricity markets to better align individual actions with overall grid emissions outcomes.

CV: Line Roald is an Grainger Institute of Engineering Associate Professor in the Department of Electrical and Computer Engineering at University of Wisconsin—Madison. She received her Ph.D. degree in Electrical Engineering (2016) from ETH Zurich, Switzerland, and was a postdoctoral research fellow at Los Alamos National Laboratory. She is the recipient of an NSF CAREER award, the Vilas Early Career Investigator Award and several best paper awards, and serves on the National Academies Roundtable on Climate Change and AI. Her research interests center around modeling and optimization of energy systems, with a particular focus on managing uncertainty and risk from extreme weather and renewable energy variability.

Jan Peters
TU Darmstadt

Title: Inductive Biases for Robot Reinforcement Learning

Abstract: Autonomous robots that can assist humans in situations of daily life have been a long standing vision of robotics, artificial intelligence, and cognitive sciences. A first step towards this goal is to create robots that can learn tasks triggered by environmental context or higher level instruction. However, learning techniques have yet to live up to this promise as only few methods manage to scale to high-dimensional manipulator or humanoid robots. In this talk, we investigate a general framework suitable for learning motor skills in robotics which is based on the principles behind many analytical robotics approaches. To accomplish robot reinforcement learning learning from just few trials, the learning system can no longer explore all learn-able solutions but has to prioritize one solution over others – independent of the observed data. Such prioritization requires explicit or implicit assumptions, often called ‘induction biases’ in machine learning. Extrapolation to new robot learning tasks requires induction biases deeply rooted in general principles and domain knowledge from robotics, physics and control. Empirical evaluations on a several robot systems illustrate the effectiveness and applicability to learning control on an anthropomorphic robot arm. These robot motor skills range from toy examples (e.g., paddling a ball, ball-in-a-cup) to playing robot table tennis, juggling and manipulation of various objects.

CV: Jan Peters is a full professor (W3) for Intelligent Autonomous Systems at the Computer Science Department of TU Darmstadt since 2011, head of the research department Systems AI for Robot Learning (SAIROL) at the German Research Center for Artificial Intelligence (DFKI) since 2022, and a founding research faculty member of the Hessian Center for Artificial Intelligence. His honors include the Dick Volz Best US PhD Thesis Runner-Up Award (2007), the RSS Early Career Spotlight, the INNS Young Investigator Award, the IEEE RAS Early Career Award, numerous best paper awards, and an ERC Starting Grant (2015). He is a Fellow of the IEEE (2019), ELLIS (2020), and AAIA (2021). His former group members have gone on to faculty positions at leading universities in the USA, Japan, and Europe, postdoctoral positions at MIT, CMU, and Berkeley, and leadership roles at top AI companies including Amazon, Boston Dynamics, Google, and Meta.
Jan Peters studied Computer Science, Electrical, Mechanical and Control Engineering at TU Munich, FernUni Hagen, NUS, and USC, earning four Master’s degrees and a PhD in Computer Science from USC. Before joining TU Darmstadt, he led research groups on Machine Learning for Robotics at the Max Planck Institutes for Biological Cybernetics (2007–2010) and Intelligent Systems (2010–2021).

Na Li
Harvard University

Title: From Generative Models to Control: Representation-based Reinforcement Learning in Physical Systems

Abstract: The explosive growth of machine learning and data-driven methodologies has revolutionized numerous fields. Yet, translating these successes to dynamical physical systems remains a significant challenge, hindered by the complexity, uncertainty, and safety-critical nature of such environments. In this talk, we present a unified framework that bridges this gap by introducing novel generative representations for reinforcement learning and control. On the critic side, we develop a structured representation of system dynamics that focuses on modeling how actions influence future state distributions. This transition-based perspective enables the design of nonlinear stochastic control and reinforcement learning algorithms that are efficient, safe, robust, and scalable, with provable guarantees. On the actor side, we represent stochastic feedback policies using diffusion-based generative models, treating control as a generative process. This approach leads to new methods for policy optimization, while providing a flexible and expressive framework for decision-making in dynamical systems. We further demonstrate how these representations help close the sim-to-real gap, improve data efficiency in imitation learning, and enable scalable computation of localized policies for large-scale nonlinear networked systems, with applications including robotics and energy systems.

CV: Na Li is a Winokur Family Professor of Electrical Engineering and Applied Mathematics at Harvard University. She received her Bachelor’s degree in Mathematics from Zhejiang University in 2007 and Ph.D. degree in Control and Dynamical systems from California Institute of Technology in 2013. She was a postdoctoral associate at the Massachusetts Institute of Technology 2013-2014. She has held a variety of short-term visiting appointments including the Simons Institute for the Theory of Computing, MIT, Google Brain, and MERL. Her research lies in the control, learning, and optimization of networked systems, including theory development, algorithm design, and applications to real-world cyber-physical societal systems. She is an IEEE fellow and a senior editor of IEEE Transactions on Control of Network Systems. She was an associate editor for IEEE Transactions on Automatic Control, Systems & Control Letters, IEEE Control Systems Letters and also served on the organizing committee for a few conferences and workshops such as IEEE CDC, AMC E-energy, and NSF workshop on Reinforcement Learning. She received the NSF career award, AFSOR Young Investigator Award, ONR Young Investigator Award, Donald P. Eckman Award, McDonald Mentoring Award, IEEE CSS Distinguished Lecturer, IFAC Distinguished Lecturer, IFAC Manfred Thoma Medal, Ruberti Young Researcher Prize, along with other awards.

Henrik Sandberg
KTH Royal Institute of Technology

Title: Resilient and Secure Control of Cyber-Physical Systems: Limits and Data-Driven Approaches

Abstract: Recent cyberattacks on critical infrastructure—most notably the Industroyer cyberattack—have exposed the vulnerability of modern energy systems to adversarial manipulation of control and monitoring components. While conventional control systems are designed to handle disturbances and faults, coordinated cyberattacks pose fundamentally different challenges that existing safety mechanisms only partially address. This has motivated the development of resilient control frameworks that ensure graceful performance degradation and recovery under attack.

This lecture takes a control-theoretic perspective on modeling, detecting, and mitigating attacks in cyber-physical systems. We review representative attack scenarios and discuss system architectures, constraints, and the evolving attack surface. We then present model-based tools to characterize fundamental limits of detection and mitigation, and conclude with recent advances in data-driven approaches for improving security and resilience.

CV: Henrik Sandberg is Professor and Deputy Head at the Department of Decision and Control Systems, KTH Royal Institute of Technology, Stockholm, Sweden. He received the M.Sc. degree in engineering physics and the Ph.D. degree in automatic control from Lund University, Lund, Sweden, in 1999 and 2004, respectively. From 2005 to 2007, he was a Postdoctoral Scholar at the California Institute of Technology, Pasadena, USA. In 2013, he was a Visiting Scholar at the Laboratory for Information and Decision Systems (LIDS) at MIT, Cambridge, USA. He has also held visiting appointments at the Australian National University and the University of Melbourne, Australia. His current research interests include security of cyber-physical systems, power systems, model reduction, and fundamental limitations in control. Dr. Sandberg was a recipient of the Best Student Paper Award from the IEEE Conference on Decision and Control in 2004, an Ingvar Carlsson Award from the Swedish Foundation for Strategic Research in 2007, and a Consolidator Grant from the Swedish Research Council in 2016. He has served on the editorial boards of IEEE Transactions on Automatic Control and the IFAC Journal Automatica. He is a Member of the IEEE CSS Board of Governors, is a Fellow of the Royal Swedish Academy of Engineering Sciences, and a Fellow of the IEEE.

Stefano di Cairano
Mitsubishi Electric Research Laboratories, Cambridge

Title: Autonomy for a Sustainable Society: What Role can Control Research Play?

Abstract: A future society in which autonomous systems contribute pervasively to sustainability, safety, and security is rapidly becoming a reality. From intelligent transportation to automated monitoring and assistive robotics, autonomy is transitioning from concept to deployment. In this context, control can play a central role by providing principled system-level design, robustness to uncertainty, and the ability to guarantee performance and safety in complex, real-world environments.

At the same time, autonomous systems increasingly rely on the tight integration of control with perception, learning, and decision-making components, often driven by data and artificial intelligence. This evolution may require revisiting established design paradigms.

Drawing on examples from industry research spanning transportation and logistics, automated monitoring, and space exploration and utilization —some of which are close to or already in deployment— I will discuss how the strengths of control can be leveraged to enable reliable and scalable autonomy, as well as emerging research opportunities to address the associated challenges.

CV: Stefano Di Cairano received the Master’s (Laurea) and the Ph.D. degrees in information engineering in 2004 and 2008, respectively, from the University of Siena, Italy. During 2008-2011, he was with Powertrain Control R&A, Ford Research and Advanced Engineering, Dearborn, MI, USA. Since 2011, he is with Mitsubishi Electric Research Laboratories, Cambridge, MA, USA, where he is currently a Deputy Director, and a Distinguished Research Scientist.

His research focuses on control and decision-making strategies for autonomous systems in transportation, factory automation and aerospace. His research interests include model predictive control, constrained control, path planning, hybrid systems, and particle filtering. He has authored/coauthored more than 300 peer-reviewed papers in journals and conference proceedings and is an inventor in more than 100 patents. Dr. Di Cairano is a Fellow of IEEE, a winner of the 2024 IEEE T-ASE best new application paper award, co-author in best student papers awards at IEEE conferences, and won several Mitsubishi Electric internal awards for transferring research into real world products.

Richard Braatz
MIT, USA

Title: Machine Learning-based Lifetime Prediction and Charging Optimization of Lithium-ion Batteries

Abstract: This presentation describes advances in machine learning-based techniques for addressing systems problems that
arise for lithium-ion batteries. The specific systems problems include the prediction and classification of battery cycle lifetime (aka remaining useful life), the determination of optimal charging protocols, and the identification of fundamental physicochemical
expressions for electrochemical kinetics, thermodynamics, and mass transfer from real-time video imaging.

CV: Richard D. Braatz is the Edwin R. Gilliland Professor at the Massachusetts Institute of Technology (MIT) where he does research in robust control theory and its application to advanced manufacturing systems. He received an MS and PhD from the California Institute of Technology and was a Professor at the University of Illinois at Urbana-Champaign and a Visiting Scholar at Harvard University before moving to MIT.

His past professional service includes Editor-in-Chief of IEEE Control Systems Magazine, President of the American Automatic Control Council, and General Chair of the IEEE Conference on Decision and Control and the American Control Conference. Honors include the AACC Donald P. Eckman Award, the Curtis W. McGraw Research Award from the Engineering Research Council, the Antonio Ruberti Young Researcher Prize, and best paper awards from IEEE- and IFAC-sponsored control journals. He is a Fellow of IEEE and IFAC and a member of the U.S. National Academy of Engineering.