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Seminar @ Cornell Tech: Fei Miao

Monday, March 27, 2023 at 10:30am to 11:30am

Cornell Tech, Bloomberg Center, Room 401

Learning and Control for Safety, Efficiency, and Resiliency of Cyber-Physical Systems

The rapid evolution of ubiquitous sensing, communication, and computation technologies has contributed to the revolution of cyber-physical systems (CPS). Learning-based methodologies are integrated to the control of physical systems and provide tremendous opportunities for AI-enabled CPS. However, existing networked CPS decision-making frameworks lack understanding of the tridirectional relationship among communication, learning and control. It remains challenging to leverage the communication capability for the learning and control methodology design of CPS, to improve the safety, efficiency, and robustness of the system. In the first part of the talk, we present our research contributions on learning and control with information sharing for networked CPS. We design the first uncertainty quantification method for collaborative perception of connected autonomous vehicles (CAVs), and show the accuracy improvement and uncertainty reduction performance of our method. To utilize the information shared among agents, we then develop a safe and scalable deep multi-agent reinforcement learning (MARL) algorithms to improve system safety and efficiency. We validate the benefits of communication in MARL especially for CAVs under challenging mixed traffic scenarios. To motivate agents to communicate and coordinate, we design a novel stable and efficient Shapley value-based reward reallocation scheme for MARL. Finally, considering the complicated system dynamics and state information uncertainties from sensors and learning-based perception of networked CPS, we present our contribution to robust MARL methods, including formal analysis on the solution concept of MARL under state uncertainties and state perturbations. In the second part of the talk, we briefly present our research contributions on autonomous mobility-on-demand (AMoD) systems for fair, efficient, and sustainable transportation, with data-driven robust optimization theorems, algorithm design and real-world data validations. We will also highlight our research results on CPS security and our future work to bridge the gap between theories and real-world applications for CPS. 

 

Speaker Bio

Fei Miao is an Assistant Professor of the Department of Computer Science & Engineering, a Courtesy Faculty of the Department of Electrical & Computer Engineering, University of Connecticut since 2017. She is also affiliated to the Institute of Advanced Systems Engineering and Eversource Energy Center. She was a postdoc researcher at the GRASP Lab and the PRECISE Lab of the University of Pennsylvania from 2016 to 2017. She received the Ph.D. degree and the Best Doctoral Dissertation Award in Electrical and Systems Engineering, with a dual M.S. degree in Statistics from the University of Pennsylvania in 2016. She received the B.S. degree in Automation from Shanghai Jiao Tong University. Her research focuses on multi-agent reinforcement learning, robust optimization, uncertainty quantification, and control theory, to address safety, efficiency, robustness, and security challenges of cyber-physical systems, for application areas such as connected autonomous vehicles, intelligent transportation systems, transportation decarbonization, and smart cities. Dr. Miao is a receipt of the NSF CAREER award and a couple of other awards from NSF and DOE. She received the Best Paper Award and Best Paper Award Finalist at the 12th and 6th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) in 2021 and 2015, Best paper Award at the 2023 AAAI DACC workshop, respectively.

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Event Type

Seminar

Departments

Cornell Tech

University Themes

Research, Cornell in New York City

Tags

CornellTech

Contact E-Mail

kr469@cornell.edu

Contact Name

Katherine Rincon

Speaker

Fei Miao

Speaker Affiliation

University of Connecticut

Disability Access Information

Available Upon Request

Open To

Faculty, Master's Students, PhDs, Postdocs

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