ECE Departmental Seminar

Towards a Self-adaptive Smart City: Collaboratively Integrating Sensing, Learning and Actuation for Large-scale Urban Monitoring

Prof. Susu Xu
Department of Civil Engineering, Stony Brook University

Friday, 4/30/21, 1:00pm
Online 
To obtain access the Zoom link for this seminar, please click here to register.

Abstract: With increasing populations and demand of high-quality urban services, there is an urgent need of building a “self-adaptive” city which can autonomously adapt its monitoring and management strategies for urban infrastructure systems under constantly changing dynamics. The recent rapid development of sensor networks and 5G technologies are enabling large-scale multi-source data and real-time multi-agent control. But the large-scale and interdependent physical infrastructure systems pose challenges to data-driven monitoring and management strategies. For instances, how to design low-cost paradigms for large-scale and complex infrastructure sensing, how to capture and analyze the physical dynamic interplay between infrastructure systems from the noisy and incomplete data, how to timely react to changes of urban dynamics, and more importantly, how to automate the process of sensing, learning and actuation to improve the quality of the urban services.

In this talk, I will introduce a framework that collaboratively integrates resource-aware sensing, physics-informed learning and incentive mechanisms for large-scale urban monitoring and management. First, I will talk about my works on urban crowdsensing systems. The system utilizes low-cost sensors mounted on individual mobile devices and vehicles to automatically sense spatio-temporal urban information, and learn real-time underlying urban dynamics (e.g., human mobility, air quality, traffic congestion, road deterioration, etc.). Then I will introduce my new theory and algorithm on incentivizing large-scale vehicle mobilities and human activities to timely react to the detected changes in urban systems and maintain a long-term optimal urban monitoring system. I will introduce the first deployed vehicular urban crowdsensing system built on more than 200 taxis in China, which has already run a total mobile mileage of 418,000km and collected half-billion urban data points. Further, I will briefly mention my research on physics-informed learning and distributed learning algorithms development for efficient understanding of large-scale urban systems.

Bio: Dr. Susu Xu is an assistant professor at Department of Civil Engineering, Stony Brook University. She received her Ph.D. in Civil Engineering and Master’s degree in Machine Learning from Carnegie Mellon University, her bachelor’s degree from Tsinghua University. She has been postdoctoral research fellow at Stanford University and research scientist at the AI research team in Qualcomm Technologies. Her research focuses on collaboratively integrating crowdsensing, physics-informed machine learning, and incentive mechanisms for enabling self-adaptive smart urban infrastructure systems and improving the efficiency, reliability, and sustainability of cities. She received the Best Paper Award at the IEEE International Conference of Machine Learning and Applications (ICMLA) in 2018, and the champion of NeurIPS 2018 Adversarial Vision Challenge. She is the recipient of MIT CEE Rising Star, Dowd Fellowship, Liang Ji-Dian Graduate Fellowship, and CMU CIT Dean Fellowship. She has served as the chair of IJCAI/ACM Ubicomp Continual and Multimodal Learning workshop as well as the committee member of Qualcomm Innovation Fellowship, IJCAI, AAAI, and ACM SenSys.