• Title: Brain and Cognitive Dynamics, and Control-Theoretic States of Mind

    Abstract: The intersection of control engineering and neuroscience is rich and growing. The brain, fundamentally, is a dynamical system, and from this perspective, the formalisms of control theory provide vehicles for the processes of analysis and design. Design is where much effort at the control-neuroscience intersection has historically been directed, in applicative contexts such as clinical brain stimulation and related neurotechnology development. Analysis, on the other hand, represents an area of substantial recent momentum, as neuroscientists seek new and improved understanding of the complexities of brain function. In this talk, I will discuss our work bridging dynamics, control, and brain and cognitive science. I will highlight the importance of analysis-design synergy in this context, and the question of defining control objectives for cognitive enhancement. Engaging this question requires us to convert abstract cognitive endpoints (i.e., our ability to perceive, think and reason) into mathematically sound descriptions of neural dynamics. I will describe our efforts in this area, through the development of new methods for whole-brain modeling and system identification, enabling us to formally analyze brain states, dynamics, and their person-to-person variation. I will discuss the insights that this analysis provides regarding the intrinsic mechanisms by which the brain enables key cognitive functions, such as memory and attention, and in turn, how they impact our pursuit of control design for exogenous neuromodulation.

  • Title: The Power of Prediction for Safe Learning-Based Control

    Abstract: Learning has established a firm foothold in modern control. While integrating data-driven algorithms into autonomous systems offers unprecedented capabilities, deploying learning-based controllers in safety-critical domains poses significant risks. Optimization-based control frameworks -such as Model Predictive Control- leverage prediction to evaluate future trajectories and enforce physical constraints. However, classical concepts often struggle when operating in complex, unmodeled, or changing environments. This talk explores how the fundamental principle of model-based prediction can address key challenges in learning-based control.  First, we revisit how safety can be guaranteed through prediction and investigate this mechanism beyond classical predictive control. Given that uncertainty remains the central challenge for prediction, the core of this presentation addresses how learning-based models can enhance predictive capabilities safely and efficiently. Specifically, we examine techniques for predicting uncertainty, adapting models online, and enabling safe exploration. Combining theoretical insights with computational methods and real-world applications, this talk highlights how the synergy of prediction, optimization, and learning supports safe, high-performance
    autonomy.

  • Title: Bellman, Lyapunov, and the Quest for Safe, Near-Optimal Control

    Abstract: Recent developments in data-driven control, such as reinforcement learning, have spurred a renewed interest in optimal control and its approximate, near-optimal solutions, including value iteration (VI) and policy iteration (PI). As the community pushes to implement these algorithms on safety-critical systems, foundational questions regarding the stability, robustness, and safety of the resulting control laws become paramount. Intriguingly, even in the absence of learning, significant gaps remain in our fundamental understanding of stability and robustness for large classes of costs and dynamical models. This lecture presents a comprehensive overview of recent advancements in guaranteeing the stability of various optimal and near-optimal control laws for discrete-time linear and nonlinear systems. We will focus precisely on the intersection of Bellman’s dynamic programming principles and Lyapunov stability theory, with the ultimate goal of establishing a unified framework for simultaneous stability and near-optimality analysis. Finally, we will demonstrate how these theoretical insights directly apply to popular algorithmic techniques like VI and PI.

    Biographical Information: Dragan Nešić is a Professor in the Department of Electrical and Electronic Engineering at the University of Melbourne, where he previously served for 15 years as Deputy Head (Research) and as Associate Dean (Research) for the Faculty of Engineering and Information Technology. A Fellow of both the IEEE and IFAC, Professor Nešić has served as a Distinguished Lecturer for the IEEE Control Systems Society (CSS) and on its Board of Governors, as well as on various IEEE and IFAC technical committees. He is the recipient of numerous prestigious honors, including the George S. Axelby Outstanding Paper Award, a Doctorate Honoris Causa from the University of Lorraine, the Humboldt Research Award, an Alexander von Humboldt Research Fellowship, an ARC Future Fellowship and an Australian Professorial Fellowship. Professor Nešić currently serves as a Senior Editor for the IEEE Transactions on Automatic Control. His extensive editorial background includes service as an Associate Editor for Automatica, IEEE Transactions on Control of Network Systems, IEEE Transactions on Automatic Control, European Journal of Control, and Systems & Control Letters. He has contributed to the International Program Committees of many flagship conferences and served as General Co-Chair for the 2017 IEEE Conference on Decision and Control (CDC). His research interests lie in the broad areas of nonlinear and hybrid systems, including Lyapunov stability theory, optimization-based control, time-scale separation (singular perturbations and averaging), networked control systems, event-triggered control and estimation, extremum seeking control, and applications in neuroscience and energy systems.

  • Title: Information is in Control

    Abstract: Control theory has transformed engineering over the past half-century, with aerospace among its defining successes. Over the past two decades, its reach has expanded into many fields including biology, energy, transportation, and networked systems. Today, AI is creating a new class of applications that calls for a new control paradigm.  Digital platforms have become central mechanisms for coordinating human behavior. They shape how we navigate cities, consume energy, shop, allocate resources, and operate markets. AI is dramatically expanding their capabilities, enabling them to learn from data, interact continuously with users, and orchestrate the decisions of millions of autonomous agents.

    Platforms such as navigation and e-commerce systems observe agents’ actions and assemble critical information about the state of the system. Yet they generally lack access to agents’ private preferences, objectives, and constraints, limiting their ability to align behavior effectively. Navigation platforms, for example, rely largely on recommendations derived from public information. Such recommendations can synchronize behavior and ultimately degrade system performance. This information asymmetry, combined with the strategic behavior of agents who possess private information, calls for a new control paradigm in which coordination occurs through an information market.  In an information market, the central challenge shifts from designing control policies for individual agents to designing personalized information that induces autonomous agents to collectively achieve a system objective. This requires appropriate incentives for agents to reveal their private information. The monetization of information introduces a new feedback loop involving both information and payments, effectively creating a market for data. Designing such a market requires the simultaneous design of elicitation mechanisms, personalized information, and learning algorithms in a distributed environment.  The same architecture, however, can allow digital platforms to exploit information asymmetries and exercise market power. Recent class-action lawsuits against major e-commerce platforms illustrate the risks of manipulating information to influence prices, demand, and consumer behavior. Understanding this duality is essential to designing AI-enabled platforms that preserve the societal benefits of personalized recommendations while limiting the risks of concentrated informational power.

    In this talk, I will develop this paradigm and explain how the principles of control theory provide a natural framework for addressing its central challenges.