Speaker Prof. Naomi Ehrich Leonard
Mechanical and Aerospace Engineering,
Princeton University, USA
Date｜Time Feb 27 (Friday), 2026｜09:00
Zoom https://snu-ac-kr.zoom.us/my/jingyu.lee
Abstract
It remains an open question how a multiagent system, with no centralized control and possibly severe limitations on communication, sensing, and computational power, is capable of the fast and flexible decision-making required to successfully operate in environments with uncertainty, variability, and rapid change. I will present decision dynamics that draw on biophysical models from computational neuroscience and provide the means to study and design fast, flexible, and frugal multiagent decision-making and control. Our approach to analysis allows rigorous and systematic investigation and leveraging of distinguishing features of the decision dynamics: deadlock can be reliably broken as fast as it becomes costly; sensitivity to distributed stimulus can be tuned as context and environment change; social heterogeneity can enhance stability and flexibility; and excitability (spiking) can provide sparsity and intermittency leading to superior agility and frugality. I will discuss the significance and application of these results for the study and design of collective intelligence in nature and technology.
Biography
Naomi Ehrich Leonard is Chair and Edwin S. Wilsey Professor of Mechanical and Aerospace Engineering, associated faculty with the Program in Applied and Computational Mathematics and the Biophysics Graduate Program, and affiliated faculty with the Princeton Neuroscience Institute at Princeton University. She is Founding Director of creativeX, a Princeton engineering-and-the-arts collective, and Founding Editor of Annual Review of Control, Robotics, and Autonomous Systems. Leonard received her B.S.E. in Mechanical Engineering from Princeton University and her Ph.D. in Electrical Engineering from the University of Maryland. She is a MacArthur Fellow, member of the American Academy of Arts and Sciences, Fellow of the ASME, IEEE, IFAC, and SIAM, and recipient of the 2023 IEEE Control Systems Award, 2024 Richard E. Bellman Control Heritage Award, and 2025 IEEE George S. Axelby Outstanding Paper Award. Her current research focuses on dynamics, control, and learning for multiagent systems on networks with application to multi-robot teams, collective animal behavior, and other networked systems in technology, nature, and the arts.
