Join us for the next talk in the 2023-2024 Distinguished Lectures Series by Prof. Mahnoosh Alizadeh of University of California, Santa Barbara
Date: March 7, 2024 at 4:00 – 5:00pm in SF1105
Title: Safe Learning Methods with Applications in Dynamic Pricing for Demand Management
Abstract: The pervasive integration of edge intelligence has provided opportunities for proactive demand management in critical infrastructure systems such as power or transportation networks. Existing approaches often involve soliciting customer preferences or navigating complex negotiation protocols to coordinate aggregate customer behavior before real-time operations, making them challenging to implement in the real world. This has ignited growing interest in data-driven methods that gradually and autonomously learn to manage demand, all without the need for direct communication with users. But such data-driven actions may lead to violation of the networks’ physical safety constraints as they are uncertain about the users’ response. This talk will explore several recent methods developed in our group which guarantee that data-driven control actions taken to change the users’ demand in safety-critical systems are network-safe regardless of model uncertainty, and discuss their accompanying theoretical performance bounds on the growth of regret.
Bio: Mahnoosh Alizadeh is an Associate Professor of Electrical and Computer Engineering at the University of California Santa Barbara. She received her Ph.D. degree in Electrical and Computer Engineering from the University of California Davis in 2014. From 2014 to 2016, she was a postdoctoral scholar at Stanford University. Her research is focused on the design of network control, learning and optimization algorithms for societal-scale cyber-physical systems. She is a recipient of the National Science Foundation CAREER award and currently serves as an AE for the IEEE Transactions on Control of Network Systems and the IEEE Open Journal of Control Systems.