Big Data Seminar Series
Big Data Seminar Series
Speaker: Prof. Fei Miao
Location: Galanti Lounge (third floor of the University Library)
Date/Time: Oct. 24 (Wednesday), at 2:00 p.m.
Title: Data-Driven Dynamic Robust Resource Allocation for Efficient Transportation
Abstract: Ubiquitous sensing in smart cities enables large-scale multi-source data collected in real-time, poses several challenges and requires a paradigm-shift to capture the complexity and dynamics of systems. Data-driven cyber-physical systems (CPSs) integrating machine learning, optimization, and control are highly desirable for this paradigm-shift, since existing model-based techniques of CPSs become inadequate. For instance, how to identify, analyze the dynamical interplay between urban-scale phenomena (such as mobility demand and supply) from data, and take actions to improve system-level service efficiency is still a challenging problem in transportation systems. In this talk, we present a data-driven dynamic robust resource allocation framework to match supply towards spatial-temporally uncertain demand, while seeking to reduce total resource allocation cost. First, we present a receding horizon control framework that incorporates large-scale historical and real-time sensing data in demand prediction and dispatch decisions under practical constraints. However, demand prediction error is not negligible and affects the system’s performance. Therefore, with spatial-temporal demand uncertainty models constructed from data, we then develop two computationally tractable robust resource allocation methods to provide probabilistic guarantees for the system’s worst-case and expected performances. As a case study, we evaluated the proposed framework using 100GB real world taxi operational data. Lastly, I will provide an overview of my research that uses sensing data and the knowledge of the system dynamics to guarantee security and resiliency properties of CPSs.
Biography: Fei Miao is an Assistant Professor of the Department of Computer Science & Engineering, and she is also affiliated to the Department of Electrical & Computer Engineering, University of Connecticut. She received a Ph.D. degree, and the “Charles Hallac and Sarah Keil Wolf Award for Best Doctoral Dissertation” in Electrical and Systems Engineering in May 2016, with a dual Master degree in Statistics from Wharton School in August 2015, from the University of Pennsylvania, Philadelphia, USA. She received a B.S. degree with a major in Automation and a minor in Finance, from Shanghai Jiao Tong University, Shanghai, China in June 2010. Before joining Uconn, she was a postdoc researcher at the GRASP Lab and the PRECISE Lab of Upenn, from September 2016 to August 2017. She was a Best Paper Award Finalist at the 6th ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS) in 2015.