Speakers

Michael Muehlebach, Max Planck, Tuebingen

Systems Theory for Machine Learning

Michael Muehlebach studied mechanical engineering at ETH Zurich and specialized in robotics, systems, and control during his Master’s degree. He received the B.Sc. and the M.Sc. in 2010 and 2013, respectively, before joining the Institute for Dynamic Systems and Control for his Ph.D. He graduated under the supervision of Prof. R. D’Andrea in 2018 and joined the group of Prof. Michael I. Jordan at the University of California, Berkeley as a postdoctoral researcher. In 2021 he started as an independent group leader at the Max Planck Institute for Intelligent Systems in Tuebingen, where he leads the group "learning and dynamical systems." He is interested in a variety of subjects, including machine learning, dynamical systems, and optimization. During his Ph.D. he developed approximations to the constrained linear quadratic regulator problem, a central problem in control theory, and applied these to model predictive control. He also designed control and estimation algorithms for balancing robots and flying machines. His more recent work straddles the boundary between machine learning and optimization, and includes the analysis of momentum-based and constrained optimization algorithms from a dynamical systems point of view. He received the Outstanding D-MAVT Bachelor Award for his Bachelor’s degree and the Willi-Studer prize for the best Master’s degree. His Ph.D. thesis was awarded with the ETH Medal and the HILTI prize. He was also awarded a Branco Weiss Fellowship, Emmy Noether Fellowship, and an Amazon research grant.

Carsten W. Scherer, University of Stuttgart

Structure, Analysis and Design of Optimization Algorithms

Carsten W. Scherer received his Ph.D. degree in mathematics from the University of Würzburg (Germany) in 1991. In 1993, he joined Delft University of Technology (The Netherlands) where he held positions as an assistant and associate professor. From 2001 until 2010 he was a full professor at the Delft Center for Systems and Control at Delft University of Technology. Since March 2010 he holds the SimTech Chair for Mathematical Systems Theory in the Department of Mathematics at the University of Stuttgart (Germany). Dr. Scherer acted as the chair of the IFAC technical committee on Robust Control, and he has served as an associated editor for the IEEE Transactions on Automatic Control, Automatica, Systems and Control Letters and the European Journal of Control. He is an IEEE fellow "for contributions to optimization-based robust controller synthesis." His main research activities cover a broad range of topics in applying optimization techniques for developing new advanced controller design algorithms and their application to mechatronics and aerospace systems.

Giovanni Russo, Università di Salerno

The Distributionally Robust Free Energy Principle for Learning and Control

Giovanni Russo is an Associate Professor of Automatic Control at the University of Salerno, Italy. He was previously with the University of Naples Federico II (Ph.D. in 2010), Italy; Ansaldo STS (System Engineer/Integrator of the Honolulu Rail Transit Project in 2012–2015), Hawaii, USA; IBM Research Ireland (Research Staff Member in Optimization, Control and Decision Science, from 2015 to 2018) and University College both in Dublin, Ireland (in 2018–2020). Dr. Russo has served as Associate Editor for the IEEE Transactions on Circuits and Systems I: regular papers (2016–2019) and the IEEE Transactions on Control of Network Systems (2017–2023). Since January 2024, Dr. Russo is serving as Senior Editor for the IEEE Transactions on Control of Network Systems. He is also a member of the Board of Directors of the Modelling and Engineering Risk and Complexity Ph.D. program at the School for Advanced Studies in Naples. His research interests include control in the space of densities, contraction theory, analysis/control of nonlinear systems and networks, data-driven control, neuro-inspired computation and learning.

Maurice Heemels, TU Eindhoven

Feedback Optimization with State Constraints through Control Barrier Functions

Maurice Heemels received M.Sc. (mathematics) and Ph.D. (EE, control theory) degrees (summa cum laude) from the Eindhoven University of Technology (TU/e) in 1995 and 1999, respectively. From 2000 to 2004, he was with the Electrical Engineering Department, TU/e, as an assistant professor, and from 2004 to 2006 with TNO-Embedded Systems Institute as a Research Fellow. Since 2006, he has been with the Department of Mechanical Engineering, TU/e, where he is currently a Full Professor and Vice-Dean. He held visiting professor positions at ETH, Switzerland (2001), UCSB, USA (2008) and University of Lorraine, France (2020). He is a Fellow of IEEE and IFAC, and was the chair of the IFAC Technical Committee on Networked Systems (2017-2023). He served/s on the editorial boards of Automatica, Nonlinear Analysis: Hybrid Systems (NAHS), Annual Reviews in Control, and IEEE Transactions on Automatic Control, and is the Editor-in-Chief of NAHS as of 2023. He was a recipient of a personal VICI grant awarded by NWO (Dutch Research Council) and an ERC Advanced Grant. He was the recipient of the 2019 IEEE L-CSS Outstanding Paper Award and the Automatica Paper Prize 2020-2022. He was elected for the IEEE-CSS Board of Governors (2021-2023) and will be the IPC chair for the IFAC World Congress 2029 in Amsterdam, The Netherlands. His current research includes hybrid and cyber-physical systems, networked, event-triggered and neuromorphic control systems and model predictive control and their applications.

Emiliano Dall'Anese, Boston University

Online Optimization-based Control as Interconnected Continuous-time and Discrete-time Dynamics

Emiliano Dall’Anese is an Associate Professor in the Department of Electrical and Computer Engineering at Boston University, where he is also the Associate Head of the Division of Systems Engineering. He received the Ph.D. in Information Engineering from the Department of Information Engineering, University of Padova, Italy, in 2011. He was with the University of Minnesota as a postdoc (2011-2014), the National Renewable Energy Laboratory as a senior researcher (2014-2018), and the Department of Electrical, Computer, and Energy Engineering at the University of Colorado Boulder as a faculty (2018-2024). His research interests span the broad areas of optimization, control, and systems theory; current applications include power systems and autonomous systems. He received the National Science Foundation CAREER Award in 2020, the IEEE PES Prize Paper Award in 2021, the IEEE Transactions on Control of Network Systems Best Paper Award in 2023, the IEEE PES ISGT Europe best paper award in 2024, and the Outstanding Associate Editor recognition from IEEE LCSS in 2026.

Gianluca Bianchin, UC Louvain

Analysis and Design of Online Optimization Algorithms Through the Lens of Internal Models

Gianluca Bianchin is an Assistant Professor with the ICTEAM Institute at the University of Louvain (UCLouvain), Belgium. He received the Ph.D. degree in Mechanical Engineering from the University of California, Riverside in 2020. He previously obtained the M.Sc. degree in Controls Engineering from the University of Padua, Italy, in 2014. Dr. Bianchin was a Postdoctoral Scholar in the Department of Electrical, Computer, and Energy Engineering at the University of Colorado Boulder from 2020 to 2022. He has also spent time as a Visiting Researcher at the Bosch Research and Technology Center North America and at the Pacific Northwest National Laboratory. Prof. Bianchin received the IEEE Transactions on Control of Network Systems Best Paper Award in 2023 and the Dissertation Year Award from the University of California, Riverside in 2019. Additionally, his research on secure robotic navigation was selected as the Editor’s Choice of the Month by the Elsevier journal Automatica in February 2020. His research interests include dynamical systems, control theory, and algorithmic optimization and their applications in traffic control and network infrastructures.

Ioannis Lestas, University of Cambridge

Stability, Instability and Optimal Control interpretations in Algorithms for Distributed Optimization

Ioannis Lestas is a Professor of Control Engineering at the Department of Engineering, University of Cambridge. He received the B.A. (Starred First) and M.Eng. degrees in Electrical and Information Sciences and the Ph.D. in control engineering from the University of Cambridge (Trinity College) in 2002 and 2007, respectively. His doctoral work was performed as a Gates Scholar. He has been a Junior Research Fellow of Clare College, University of Cambridge and he was awarded a five year Royal Academy of Engineering research fellowship. He is also the recipient of a five year ERC starting grant, and an ERC proof of concept grant. He is currently serving as Associate Editor for the IEEE Transactions on Automatic Control, the IEEE Transactions on Smart Grid, and as Senior Editor for the IEEE Transactions on Control of Network Systems. His research interests include the control of large-scale networks with applications in power systems and smart grids.

François Glineur, UC Louvain

Computer-aided Analysis of Optimization Algorithms using Performance Estimation Problems

François Glineur earned dual engineering degrees from CentraleSupélec and Université de Mons in 1997, and a PhD in Applied Sciences from the latter institution in 2001. After visiting Delft University of Technology and working as a post-doctoral researcher at McMaster University, he joined Université catholique de Louvain, where he is currently a full professor of applied mathematics in the Engineering School. He is also a member of the Center for Operations Research and Econometrics and the Institute of Information and Communication Technologies, Electronics and Applied Mathematics. He served as the Engineering School's vice-dean between 2016 and 2020. His primary areas of interest in research are optimization models and methods, with a particular emphasis on convex optimization and algorithmic efficiency. He received the 2017 Optimization Letters best paper award for the worst-case analysis of gradient descent with line search. He is also interested in the use of optimization in engineering, as well as nonnegative matrix factorization and its application to data analysis.

Runyu (Cathy) Zhang, MIT

Solving Constrained Optimization From a Control-theoretic Perspective: Theory, Acceleration and Zeroth-order Extensions

Runyu Zhang is a postdoc researcher at MIT in the Department of Civil and Environmental Engineering and the Laboratory for Information & Decision Systems. Before joining MIT, she obtained her Ph.D. degree at Harvard University, School of Engineering and Applied Sciences. Her research interests lie broadly in learning-based control, reinforcement learning, game theory and optimization, with particular focus on multi-agent systems. She has won the MIT Postdoctoral Fellowship for Engineering Excellence, was selected as the EECS rising star in 2024, and was a finalist of the Two Sigma Diversity PhD Fellowship in 2022.

Andrea Martin, KTH Royal Institute of Technology

Learning to Accelerate Fixed-point Iterations with Guarantees

Andrea Martin is a postdoctoral researcher in the Department of Decision and Control Systems (DCS) at KTH Royal Institute of Technology. He received his Ph.D. in Robotics, Control, and Intelligent Systems from EPFL in 2025. Before that, he obtained a B.Sc. in Information Engineering and two M.Sc. degrees in Automation Engineering and Automatic Control and Robotics from the University of Padova and the Polytechnic University of Catalonia through the TIME double degree program. His research interests lie at the intersection of control theory, optimization, and machine learning. He was awarded the Digital Futures Postdoctoral Fellowship in 2024 and the Swiss National Science Foundation Postdoc.Mobility Fellowship in 2025.

Veronica Centorrino, ETH Zurich

On Contracting Dynamics for Convex Optimization

Veronica Centorrino is a Postdoctoral Researcher at the Automatic Control Laboratory, ETH Zürich. She received her Bachelor's and Master's degrees in Mathematics from the University of Catania, Italy, and her PhD in Systems and Control Engineering in 2025 at the Scuola Superiore Meridionale, Naples. She held research fellowships at the Italian National Institute for Geophysics and Volcanology (2020) and at DIEM, University of Salerno (2024–2025). In 2024, she received the IEEE Control Systems Letters Outstanding Paper Award.

Her primary research interests lie at the intersection of control theory and applied mathematics, focusing on robust nonlinear control, algorithmic systems theory, optimization, and bio-inspired neural networks.

Ezzat Elokda, KTH Royal Institute of Technology

Evolutionary Dynamics: The Systems Theory of Finding Nash Equilibria in (Dynamic) Population Games

Ezzat Elokda's research focuses on human aspects of control in applications where control algorithms must trade-off human needs, and combines techniques from control theory, dynamic game theory, and welfare economics. He is currently a postdoctoral researcher in the Department of Decision and Control Systems (DCS), KTH. He received his B.ASc. in Mechatronics Engineering at the University of Waterloo in 2014, his M.Sc. in Robotics, Systems & Control at ETH Zurich in 2020, and his PhD at the Automatic Control Lab and the Institute for Dynamic Systems & Control, ETH Zurich, in 2025. From 2014-2018, he held various industrial control positions. He is recipient of the IFAC CPHS Young Author Award (2024) and the ETH Medal for his master’s thesis (2021).