Fei Miao

Assistant Professor at UConn CSE

Lecture Information:
  • December 4, 2020
  • 2:00 PM
  • Zoom: Contact SCIS coordinator (Dong Chen) for zoom credentials if you did not receive the email.
Fei Miao Portrait

Speaker Bio

Fei Miao is an Assistant Professor of the Department of Computer Science & Engineering, and she is also affiliated to the Institute of Advanced Systems Engineering, University of Connecticut since 2017. Her research interests lie in the intersection of control, optimization and machine learning with application in cyber-physical systems efficiency, safety and security. She has received a couple of awards from NSF, including S&AS, CPS, and S&CC programs. She received the Ph.D. degree, and the “Charles Hallac and Sarah Keil Wolf Award for Best Doctoral Dissertation” in Electrical and Systems Engineering, with a dual Master degree in Statistics of Wharton School from the University of Pennsylvania. She received the B.S. degree majoring in Automation from Shanghai Jiao Tong University. She was a postdoc researcher at the GRASP Lab and the PRECISE Lab of Upenn, from 2016 to 2017. She was a Best Paper Award Finalist at the 6th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) in 2015.


Ubiquitous sensing in smart cities enables large-scale multi-source data collected in real-time, poses several challenges and requires a paradigm-shift to data-driven cyber-physical systems (CPSs) that integrates optimization, control and machine learning. For instance, how to capture the complexity and analyze the dynamical interplay between urban-scale phenomena from data, and take actions to improve service efficiency and safety is still a challenging problem in transportation systems. In this talk, we first present a data-driven dynamic robust resource allocation framework for autonomous ride-sharing and carpool systems, to match vehicle supply towards both current and predicted future demand. With spatial-temporal uncertainty of demand prediction, we then prove and develop computationally tractable methods that provide probabilistic guarantees for the system’s worst-case and expected performance. A dynamic pricing model is also designed for travel time reliability during peak hours. We show that the performance of the ride-sharing system is improved based on world taxi operational data. Lastly, recent work about an information sharing and decision-making framework considering safety and efficiency of connected autonomous vehicles is introduced.