DLC Seminar: Prof. Carmen Amo Alonso
- Event Type
- Seminar/Symposium
- Sponsor
- Decision and Control Laboratory, Coordinated Science Laboratory
- Location
- B02 Coordinated Science Laboratory
- Date
- Dec 3, 2025 3:00 - 4:00 pm
- Speaker
- Prof. Carmen Amo Alonso, Schmidt Science Fellow, Stanford University
- Contact
- Max Raginsky
- maxim@illinois.edu
- Views
- 8
- Originating Calendar
- CSL Decision and Control Group
Title: Control Theory meets Artificial Intelligence
Abstract: Control theory is fundamental in the design and understanding of many natural and engineered systems, from cars and robots to power networks and bacterial metabolism. In this talk, we explore how the principles of control and dynamical systems —formalized with control theory— can also play an important role in enhancing Artificial Intelligence (AI). We argue that AI systems are themselves dynamical in nature, and that meaningful dynamics emerge at multiple levels of abstraction: from individual computational units that mix information, to their interaction with surrounding components, to full layers and deep network architectures, and ultimately to the real-world environments in which these systems operate. We discuss two specific examples of different levels of abstraction where these ideas lead to practical advances: first, how analyzing and designing individual information-mixing modules enables improved architecture construction grounded in dynamical principles; and second, how applying control-theoretic tools at the level of full network activations allows us to steer and constrain model behavior with formal guarantees. Lastly, we give an overview of how examining AI across these different scales through a unified dynamical lens reveals new opportunities for principled design, analysis, and control, ultimately moving us toward more efficient, predictable, and controllable AI systems. The aim of this talk is to illustrate the potential of viewing AI through the tools of control and dynamical systems, and to open a discussion about future research directions at the intersection of learning, control, and intelligence.
Location & Time: CSL B02, December 3, 3-4PM
Reception in CSL 154 at 2:30PM.Bio: Carmen Amo Alonso is a Schmidt Science Fellow and is affiliated with Prof. Marco Pavone's group at Stanford, where she works at the intersection of Control Theory and AI.
Before joining Stanford, Carmen spent a year as a postdoctoral fellow at the AI Center at ETH Zurich. She obtained a PhD in Control and Dynamical Systems from Caltech, where she worked under the advice of Prof. John Doyle. Her research has been awarded the best PhD dissertation of the year at Caltech, as well as with two IEEE best paper awards. Her work has also been recognized for its interdisciplinary contributions as well as its societal impact.