Plenary and Semi-Plenary Lectures

Title: The monopolist’s free boundary problem in the plane: an excursion into the economic value of private information

Abstract: The principal-agent problem is an important paradigm in economic theory for studying the value of private information: the nonlinear pricing problem faced by a monopolist is one example; others include optimal taxation and auction design. For multidimensional spaces of consumers (i.e. agents) and products, Rochet and Chone (1998) reformulated this problem as a concave maximization over the set of convex functions, by assuming agent preferences are bilinear in the product and agent parameters. This optimization corresponds mathematically to a convexity-constrained obstacle problem. The solution is divided into multiple regions, according to the rank of the Hessian of the optimizer.

Apart from four possible pathologies, if the monopolists costs grow quadratically with the product type we show that a smooth free boundary delineates the region where it becomes efficient to customize products for individual buyers. We give the first complete solution of the problem on square domains, and discover new transitions from unbunched to targeted and from targeted to blunt bunching as market conditions become more and more favorable to the seller.

Based on works-in-progress including The monopolist's free boundary problem in the plane, with Lucas O'Brien (MIT), Cale Rankin (Monash University) and Kelvin Shuangjian Zhang (Fudan University) in various combinations.

Bio: Robert John McCann is a Canadian mathematician known for his work on optimal transportation and its applications. McCann studied engineering and physics at Queen's University before graduating with a degree in math, and earned a PhD in mathematics from Princeton University in 1994. He was a Tamarkin Assistant Professor at Brown University from 1994, before joining the University of Toronto Department of Mathematics in the fall of 1998.  There he currently holds a Canada Research Chair in Mathematics, Economics, and Physics. He was an invited speaker at the International Congress of Mathematicians in Seoul in 2014, and was elected a Fellow of the American Mathematical Society in 2012, of the Royal Society of Canada in 2014, of the Fields Institute in 2015 and of the Canadian Mathematical Society in 2020. His recent awards include SIAM's WT & Idalia Reid Prize (2023), the AMS/SIAM Norbert Wiener Prize in Applied Mathematics (2025), and the CRM-Fields-PIMS Prize of the three Canadian Math Institutes (2026).  His current interests include the regularity and free boundaries in optimal transport, the economics of asymmetric information, and the development of a new nonsmooth geometry for Einstein's theory of gravity.

Title: PTNS: Phase Theory of Networks and Systems

Abstract: The phase or argument of a complex number is an elementary yet important concept, essential to almost all scientific disciplines. In SISO signal and system theory the concept of phase used to play an extremely important role. What are the phases of a complex square matrix? The answer does not seem to be unique. In this presentation, we will examine three different definitions: the principal phases (40 year old), the sectorial phases (6 year old), and the segmental phases (new born). We will compare the properties of these phase notions and use them to estimate the eigenvalue phase angles. We will also examine a network and system theory based on the phases, complementing and interconnecting with the existing gain (magnitude, singular value) based theory.

Bio: Prof. Li Qiu received his Ph.D. degree in electrical engineering from the University of Toronto in 1990. After briefly working in the Canadian Space Agency, the University of Waterloo, and the University of Minnesota, he joined the Hong Kong University of Science and Technology in 1993 and is now a Professor Emeritus in Electronic and Computer Engineering. In September 2024, he started working in the Chinese University of Hong Kong, Shenzhen, as a Presidential Chair Professor.

Prof. Qiu’s research interests include system, control, optimization theory, and mathematics for information technology, as well as their applications in manufacturing industry and energy systems. He served as an associate editor of the IEEE Transactions on Automatic Control and Automatica. He was the general chair of the 7th Asian Control Conference, which was held in Hong Kong in 2009. He was a Distinguished Lecturer from 2007 to 2010 and was a member of the Board of Governors in 2012 and 2017 of the IEEE Control Systems Society. He was the founding chairperson of the Hong Kong Automatic Control Association and a vice president of Asian Control Association. He is a Fellow of IEEE, a Fellow of IFAC, and an inaugural Fellow of Asian Control Association (ACA).

Title:  Notions of Resilience in Large-Scale Networks, with Applications to Distributed Coordination, Optimization, and Learning

Abstract: A key challenge in large-scale networks is to allow each agent to learn the true state of the world, based on its own measurements and information that it obtains from others in the network.  For example, in autonomous swarms and sensor networks, what information should the agents (drones and sensors) exchange with each other in order for the swarm to understand its environment?  Similarly, in social networks, how should individuals incorporate the opinions of their friends when updating their own beliefs about the world?  The challenge of learning in such settings is made even more difficult when malicious agents in the network spread misinformation to prevent the other agents from learning the true state.

 In this talk, we will provide an overview of classes of networked dynamics (or algorithms) that have been studied over the past decade for resilient coordination and learning in networks.  In particular, we will describe a simple general-purpose approach to enabling resilience to adversarial nodes in several classes of distributed coordination and optimization dynamics, based on each node removing a certain number of the most extreme values in its neighborhood at each time-step.  In the process, we will identify key insights about the interplay between network topology and dynamics in the context of creating more resilient networked systems.

Bio: Shreyas Sundaram is the Marie Gordon Professor in the Elmore Family School of Electrical and Computer Engineering at Purdue University, and Co-Director of the Institute for Control, Optimization and Networks (ICON).  He received his Ph.D. in Electrical Engineering from the University of Illinois at Urbana-Champaign in 2009, and was a Postdoctoral Researcher at the University of Pennsylvania from 2009 to 2010.  He was an Assistant Professor at the University of Waterloo from 2010 to 2014.  His research interests include control of distributed and multi-agent systems, fault-tolerant and secure control, game theory, and network science. Dr. Sundaram is a recipient of the National Science Foundation CAREER award, and the Intel Outstanding Researcher Award.  At Purdue, he received the Dean A. A. Potter Award for Teaching Excellence, the Dean H. T. Yang Leadership in Service Award, the HKN Outstanding Professor Award, the Outstanding Mentor of Engineering Graduate Students Award, the Hesselberth Award for Teaching Excellence, and the Ruth and Joel Spira Outstanding Teacher Award. 

Title: Enhancing Closed-Loop Analysis of Neural Network-Controlled Systems Using Quadratic Abstractions

Abstract:  This presentation introduces an approach that leverages the known properties of isolated nonlinearities—referred to as quadratic abstractions—to analyze the closed-loop behavior of dynamical systems, including stability, performance, and robustness. Different ways to reduce the conservatism inherent in traditional quadratic abstraction methods are also proposed. This framework is particularly effective for analyzing activation functions in neural network architectures, such as multilayer perceptrons (MLPs), when used to control dynamical systems, offering a robust tool for closed-loop analysis.

The proposed methodology relies on Lyapunov functions, which can be either standard quadratic or sign-indefinite quadratic forms. The latter incorporates an extended quadratic structure to further mitigate conservatism, enhancing the accuracy and applicability of the analysis. By incorporating sign-indefinite quadratic Lyapunov functions with extended quadratic structures, our methodology not only mitigates conservatism but also enhances the accuracy and applicability of the analysis, paving the way for more reliable neural network-controlled systems.

Bio: Sophie Tarbouriech received the Ph.D. degree in Control Theory in 1991 and the HDR degree (Habilitation à Diriger des Recherches) in 1998 from University Paul Sabatier, Toulouse, France. Currently, she is a Senior Researcher at CNRS and a member of LAAS- CNRS, Toulouse. Her main research interests include analysis and control of linear and nonlinear systems with constraints and hybrid dynamical systems. She is currently a recipient of the AFSR (French-Swedish Research Association) fellowship in collaboration with KTH, Sweden (2025-2026). She is Editor-in-Chief for Automatica since January 2026. She is an IFAC Pawel J. Nowacki Distinguished Lecturer (2023-2026), IEEE Fellow and IFAC Fellow. She served on the organizing committee and TPC of several IEEE and IFAC conferences, including being Publication Chair for IFAC World Congress in 2017, Program Chair for CDC 2024, Vice-program Chair for MICNON 2024 and will be Vice-program Chair for ECC 2027.

Title: relaxation of contraction theory in dynamical systems.

Abstract: Classical contraction theory is a well-established framework for analyzing nonlinear dynamics, with numerous applications in control thery. However, its strict requirements can often limit its practical applicability. This semi-plenary explores two relaxations of the contraction definition. First, we examine the transition from Incremental Global Exponential Stability to Incremental Global Asymptotic Stability. While relaxing the exponential bound provides theoretical flexibility, recent topological insights indicate that these two notions often coincide globally. Consequently, this relaxation, although analytically rigorous, results in a relatively minor shift in practical system behavior. Next, we explore the concept of k-contraction, which generalizes standard distance convergence to the shrinkage of k-dimensional volumes. We will briefly review existing analysis results before presenting recent developments in k-contraction theory.

Specifically, we will show how to derive sufficient conditions that avoid the use of compound matrices, thereby enabling the systematic design of k-contractive controllers.

Bio: Daniele Astolfi obtained a joint Ph.D. degree in Control Theory from the University of Bologna, Italy, and from Mines ParisTech, France, in 2016. For his doctoral work, he was awarded the Best Italian PhD Thesis in Control by SIDRA in 2016. Since 2018, he has been a CNRS Researcher at LAGEPP, Lyon, France. His research interests include observer design, feedback stabilization and output regulation for nonlinear systems, networked control systems, hybrid systems, and multi-agent systems. He is a IEEE Senior Member since 2026 and actively contributes to the editorial community, serving as an Associate Editor for the IFAC journal Automatica since 2018 and for the European Journal of Control since 2023.

Title: Analysis and Control in Poroelastic Systems with Applications to Biomedicine

Abstract: In biomechanics, local phenomena, such as tissue perfusion, are strictly related to the global features of the surrounding blood circulation. We propose a heterogeneous model where a local, accurate, 3D description of fluid flows through deformable porous media by means of poroelastic systems is coupled with a systemic 0D lumped model of the remainder of the circulation. This represents a multiscale strategy, which couples an initial boundary value problem to be used in a specific region with an initial value problem for the rest of the circulatory system.  We present new results on wellposedness analysis, optimal control and solution methods for this nonlinear multiscale interface coupling of PDEs and ODEs. Our results have applications in biomedicine and bioengineering, including tissue perfusion, fluid flow inside cartilages and bones, and design of bioartificial organs.

Bio: Lorena Bociu is a Professor and Associate Department Head in the Department of Mathematics at NC State University. She works in analysis, optimization, and control of partial differential equations, focusing on nonlinear structural acoustics, fluid-structure interactions, and fluid-solid mixtures. Professor Bociu received a NSF CAREER Award in 2016 and a Presidential Early Career Award in Sciences and Engineering (PECASE) in 2019. She has been a NC State Faculty Scholar since 2016. Professor Bociu is the Chair of the Working Group 7.2 (Computational Techniques in Distributed Systems) as part of IFIP TC 7 and was Chair of the SIAM Activity Group on Control and Systems Theory, 2024-2025. She is also on the Steering Committee for the AWM Research Network Women in Control (WIC). Professor Bociu is a Managing Editor for Networks and Heterogeneous Media, and an Associate Editor for the SIAM Journal on Control and Optimization (SICON), the Journal of Mathematical Analysis and Applications (JMAA), and Evolution Equations and Control Theory (EECT) Journal.

Title: Reinforcement Learning for Control: Convergence, Stability, Sample Complexity 

Abstract: I discuss a cohesive dynamical systems view of reinforcement learning as a methodology for feedback control, organized around three central questions: when learning dynamics converge, when the resulting closed loop remains stable, and how much data is required to achieve reliable performance. I present a high-level synthesis of how these strands fit together into a principled foundation for reinforcement learning in control, and highlight a few open challenges and directions.

Bio: Bahman Gharesifard is a Professor with the Department of Mathematics and Statistics at Queen's University. He was a Professor with the Electrical and Computer Engineering Department at the University of California, Los Angeles from 2021 to 2024, where he was the Area Director for Signals and Systems 2023-2024. He was an Alexander von Humboldt research fellow with the Institute for Systems Theory and Automatic Control at the University of Stuttgart in 2019-2020. He held postdoctoral positions with the Coordinated Science Laboratory at the University of Illinois at Urbana-Champaign from 2012-2013 and with the Department of Mechanical and Aerospace Engineering at University of California, San Diego 2009-2012. He received the 2019 CAIMS-PIMS Early Career Award, an Alexander von Humboldt Foundation research fellowship for experienced researchers in 2019, an NSERC Discovery Accelerator Supplement in 2019, the SIAG/CST Best SICON Paper Prize in 2021, and the Canadian Society for Information Theory Best Paper Award in 2022, and the 2024 CSS TC on Robust and Complex Systems Outstanding Student Paper Prize. He is a Senior Member of IEEE and has served on the Conference Editorial Board of the IEEE Control Systems Society, IEEE Control System Letters, and IEEE Transactions on Network Control Systems.

Title: Physics-Informed Neural Certificates for Stability and Control

Abstract:  Neural networks are universal approximators, yet learned models rarely come with rigorous guarantees. Typical statistical learning generalization bounds are often too conservative, and such probabilistic guarantees fail to certify correctness of a specific computed solution.

In this talk, we present a formal verification framework that produces a posteriori error estimates for neural network solutions of partial differential equations (PDEs) arising in systems and control. We aim to provide rigorous guarantees for the learned models rather than mere probabilistic guarantees on the PDE residuals. The framework connects machine learning with rigorous control theory by turning physics-informed neural models into certifiable computational tools.

More specifically, we study PDE characterizations that arise in stability and contraction analysis, control synthesis, and estimation for nonlinear dynamical systems. By combining formal verification tools with theoretically derived estimates, we rigorously bound approximation errors and translate these bounds into provable guarantees of stability and safety. The resulting neural certificates enable verification of regions of attraction, construction of provably correct stabilizing neural feedback controllers, and design of neural state estimators with provable error bounds.

Bio: Jun Liu received the B.S. degree in applied mathematics from Shanghai Jiao-Tong University in 2002, the M.S. degree in mathematics from Peking University in 2005, and the Ph.D. degree in applied mathematics from the University of Waterloo in 2011. Following an NSERC Postdoctoral Fellowship in Control and Dynamical Systems at Caltech, he became a Lecturer in Control and Systems Engineering at the University of Sheffield in 2012. He joined the Faculty of Mathematics of the University of Waterloo in 2015, where he is currently a Professor of Applied Mathematics and directs the Hybrid Systems Laboratory. Dr. Liu's main research interests are in the theory and applications of hybrid systems and control, including rigorous computational methods for control design with applications in cyber-physical systems and robotics. He was awarded a Marie-Curie Career Integration Grant from the European Commission in 2013, a Canada Research Chair from the Government of Canada in 2017 and 2022, an Early Researcher Award from the Ontario Ministry of Research, Innovation and Science in 2018, and an Early Career Award from the Canadian Applied and Industrial Mathematics Society and Pacific Institute for the Mathematical Sciences in 2020.  His best paper awards include the Zhang Si-Ying Outstanding Youth Paper Award, the Nonlinear Analysis: Hybrid Systems Paper Prize, and the Oded Maler Award. Dr. Liu is a senior member of IEEE, a member of SIAM, and a lifetime member of CAIMS. He has served as the Chair of the IEEE Control Systems Society Technical Committee on Hybrid Systems, as the Vice Chair of the SIAM Control and Systems Theory Activity Group,  and on the editorial boards and program committees of several journals and conferences, including Automatica, Systems & Control Letters, and Nonlinear Analysis: Hybrid Systems.

Title: Synthesis & Analysis Tools for Dynamic Games – Model Based, Data-Driven and Nonlinear Perspectives

Abstract: Dynamic games provide a powerful framework for modeling interactions between multiple decision-makers. However, as infinite-dimensional problems, they are notoriously difficult to solve, and the coupling between players can generate emergent behaviours that are difficult to predict.  In this talk, we will revisit some basics of dynamic games and (single-player) optimal control. Then, starting with the class of Linear Quadratic dynamic games, we will present iterative algorithms for obtaining feedback Nash equilibrium solutions that are amenable to both model-based and data-driven implementations. Turning our attention to general, nonlinear dynamic games we will then demonstrate how feedback Nash equilibria can be characterised in terms of invariant manifolds of a certain “lifted” dynamical system. This geometric perspective – analogous to the state/costate dynamics associated with Pontryagin’s Minimum Principle (e.g. in the context of optimal control and open-loop Nash equilibria of dynamic games) – offers two key advantages. First, it allows us to recast the problem of obtaining feedback Nash equilibria as a static optimisation problem, which lays the basis for novel computational strategies, e.g. based on gradient-descent algorithms. Second, it provides a framework to analyse interactions between players in terms of energy exchange. 

Bio: Thulasi Mylvaganam received the M.Eng. degree in Electrical and Electronic Engineering and the Ph.D. degree in Nonlinear Control and Differential Games from Imperial College London, London, U.K., in 2010 and 2014, respectively. From 2014 to 2016, she was a Research Associate with the Department of Electrical and Electronic Engineering, Imperial College London. From 2016 to 2017, she was a Research Fellow with the Department of Aeronautics, Imperial College London, UK, where she is currently Associate Professor. Her research interests include nonlinear control, optimal control, game theory, distributed control and data-driven control. She is Associate Editor of the IEEE Control Systems Letters, of the European Journal of Control, of the IEEE CSS Conference Editorial Board and of the EUCA Conference Editorial Board. She is also Vice Chair of Education for the IFAC Technical Committee 2.4 (Optimal Control) and Member of the UKACC Executive Committee.

Title: Finite Dimensional Feedback Controllers for Distributed Parameter Systems

Abstract: There are primarily two ways to obtain finite-dimensional stabilizing controllers for LTI infinite-dimensional systems: (i) approximate the plant transfer function, then design a rational controller for the approximant; (ii) design an infinite-dimensional controller for the original distributed parameter system, then approximate this controller. In both methods, approximation errors affect the robust stability and performance of the feedback system. In this talk, we discuss these methods and illustrate them on some typical examples. For method (i), we use H-infinity controllers to guarantee robust stability; for method (ii), we study the effects of approximating the infinite-dimensional stable part of a specific type of controller introduced recently.

Bio: Hitay Özbay is a Professor of Electrical and Electronics Engineering at Bilkent University, Ankara, Turkey.  He received the B.Sc., M.Eng., and PhD degrees from Middle East Technical University (1985), McGill University (1987), and the University of Minnesota (1989), respectively. His prior academic affiliations include the University of Rhode Island (1989-1990) and The Ohio State University (1991-2006), where he was a Professor of Electrical and Computer Engineering before joining Bilkent University. He also held visiting professor positions at INRIA, France (2009-2010) and at the University of California, Davis (2024-2025).  Professor Özbay was a member of the Board of Governors of the IEEE Control Systems Society (2017-2019) and a general assembly member of the European Control Association (EUCA), representing Turkey (2013-2019). He served as Associate Editor for many journals, including IEEE Transactions on Automatic Control, SIAM Journal on Control and Optimization, and Automatica. He also served as Vice-Chair of IFAC Technical Committees on Networked Control Systems (2005-2011) and Linear Control Systems (2017-2023). He is a Fellow of IEEE.

Title: Large-Scale Network Processes: Achieving Tractability via Graph Limits

Abstract: Network dynamical systems provide a versatile framework for studying the interplay between the structure of complex networks and the dynamic behavior of its constituent entities, offering insights into diverse phenomena across disciplines such as physics, biology, sociology, and engineering. As the size of the underlying network increases, however, a number of new challenges arise. For example, collecting exact network data may become too costly and planning optimal network interventions may become computationally intractable. In this talk I will show how the theory of graph limits can be used to provide new insights on graph processes evolving over large random networks. First, I will illustrate how graph limits can be used to define tractable infinite population models of network systems while maintaining agents’ heterogeneity. Second, I will illustrate how insights derived for such infinite population models can be applied to study large but finite networks. The benefit of this graph limit approach will be demonstrated for broad classes of network processes including strategic interactions, multi-agent learning and synchronization dynamics. 

Bio: Francesca Parise is an Assistant Professor in the School of Electrical and Computer Engineering at Cornell University. Before joining Cornell in July 2020, she was a postdoctoral researcher at the Laboratory for Information and Decision Systems at MIT. She defended her PhD at the Automatic Control Laboratory, ETH Zurich, Switzerland in 2016 and she received the B.Sc. and M.Sc. degrees in Information and Automation Engineering in 2010 and 2012, from the University of Padova, Italy, where she simultaneously attended the Galilean School of Excellence. Her research focuses on analysis and control of large multi-agent systems, with application to transportation, energy, social and economic networks. 

Francesca is the recipient of the NSF CAREER award, the 2024 AFOSR YIP, the Michael Tien ’72 teaching award from Cornell University, the SNSF Early and Advanced Postdoc Fellowship, the ETH Medal and the Guglielmo Marin award from the “Istituto Veneto di Scienze, Lettere ed Arti”.

Title: Control by interconnection of irreversible port-Hamiltonian systems

Abstract: Irreversible port-Hamiltonian systems (IPHS) provide a thermodynamically consistent framework for modeling multi-physical processes in which thermal phenomena play a central role. While classical passivity-based control methods have been extensively developed for reversible mechanical and electrical systems, extending these techniques to irreversible systems introduces new challenges due to the presence of entropy dynamics and the co-energy dependence of the structure matrices.
In this talk, we present a systematic control-by-interconnection methodology for IPHS that enables energy shaping and entropy assignment through modulated output feedback. A central ingredient is the identification of closed-loop invariant functions, analogous to Casimir functions in standard port-Hamiltonian theory, which allow the closed-loop Hamiltonian to coincide with thermodynamic availability functions. This provides physically meaningful Lyapunov candidates and preserves the energy–entropy structure of the system.
The methodology will be illustrated on two representative examples: a continuous stirred tank reactor and a gas-piston system. In both cases, the closed-loop system exhibits asymptotic stabilization while respecting first- and second-law constraints, and the approach naturally decouples thermodynamic and mechanical domains when appropriate. Beyond these case studies, the results highlight how irreversible thermodynamics, geometric modeling, and passivity-based control can be brought together to address complex open systems with thermal interactions.

Bio: Hector Ramirez (born in Tomé, Chile, 1981) is an Associate Professor in the Department of Electronic Engineering at Universidad Técnica Federico Santa María (UTFSM) in Valparaíso, Chile, where he also serves as Director of the Advanced Center for Electrical and Electronic Engineering (AC3E). He received his Engineering degree in Electronic Engineering and M.Sc. in Electrical Engineering from the University of Concepción (Chile), and in 2012 earned dual Ph.D. degrees in Automatic Control from Claude Bernard University (Lyon, France) and in Electrical Engineering from the University of Concepción. In 2019 he obtained the French Habilitation (HDR) from the University of Bourgogne – Franche-Comté. Dr. Ramirez’s research lies at the intersection of geometric control, port-Hamiltonian systems, nonlinear and distributed parameter systems, and the control of multi-physical processes. He has made significant contributions to irreversible port-Hamiltonian system theory, energy-based modeling, structure-preserving discretization, and stability analysis, with applications spanning thermo-electro-mechanical systems, fluid-structure interaction, and boundary control problems. Before joining UTFSM in 2018, he was Maître de Conférences at Université de Franche-Comté and a researcher at the Automatic Control and Micro-Mechatronic Systems department (AS2M) of FEMTO-ST in France. He has supervised numerous Ph.D. and M.Sc. students and taught advanced courses on port-Hamiltonian modeling, energy-based control, and distributed parameter systems in Chile and Europe. Dr. Ramirez is active in the international control community, serving on IFAC and IEEE CSS technical committees (Nonlinear Control Systems, Distributed Parameter Systems), on editorial and program committees for major conferences and workshops, and as Associate Editor for ACC, CDC, and specialist IFAC/IEEE workshops. His leadership at AC3E and his collaborations with global research centers reflect a commitment to advancing both theory and technology in control systems engineering.