ES讲堂|Real-time Distributed Decision Making in Networked Systems

北京大学工学院
2021-10-19 18:00 浏览量: 3117
讲座题目:Real-time Distributed Decision Making in Networked Systems讲座时间:2021年10月29日(周五)9:00-10:30 am线上Zoom会议注册:https://zoom.us/meeting/register/tJwpfuCqqDgsHdDlLOh-ps-A4mmo8q-Qb_-i演讲者:Na Li (黎娜)Gordon McKay professor School of Engineering and Applied Sciences, Harvard University(哈佛大学约翰·A·保尔森工程与应用科学学院)主持人:宋洁,北京大学工学院工业工程与管理系 长江特聘教授,副院长

Na Li

Gordon McKay professor

School of Engineering and Applied Sciences,Harvard University

演讲者简介

Peking University ES Seminars

Na Li is a Gordon McKay professor in Electrical Engineering and Applied Mathematics at Harvard University. She received her Bachelor degree in Mathematics from Zhejiang University in 2007 and Ph.D. degree in Control and Dynamical systems from California Institute of Technology in 2013. She was a postdoctoral associate at Massachusetts Institute of Technology 2013-2014. Her research lies in control, learning, and optimization of networked systems, including theory development, algorithm design, and applications to real-world cyber-physical societal systems. She received NSF career award (2016), AFSOR Young Investigator Award (2017), ONR Young Investigator Award (2019), Donald P. Eckman Award (2019), McDonald Mentoring Award (2020), along with some other awards.

讲座摘要

Peking University ES Seminars

Recent revolutions in sensing, computation, communication, and actuation technologies have been boosting the development and implementation of data-driven decision making, greatly advancing the monitoring and control of complex network systems. In this talk, we will focus on real-time distributed decision-making algorithms for networked systems. The first part will be on the scalable multiagent reinforcement learning algorithms and the second part will be on the model free control methods for power systems based on continuous time zeroth-order optimization methods. We will show that exploiting network structure or underlying physical dynamics will facilitate the design of scalable real-time learning and control methods.

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Peking University ES Seminars

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