KEYNOTE 4


Urban Computing: Building Intelligent Cities Using AI and Big Data

- Yu Zheng , JD.COM and Head JD Intelligent Cities Research, China

Day 4 - MDM Research III: Fri., Jun. 18
Toronto Seattle Paris Athens Beijing Brisbane
EDT (UTC-4) PDT (UTC-7) CEST (UTC+2) EEST (UTC+3) CST (UTC+8) AEST (UTC+10)
9:10 - 10:15 6:10 - 7:15 15:10 - 16:15 16:10 - 17:15 21:10 - 22:15 23:10 - 00:15

Abstract

Urban computing connects ubiquitous sensing technologies, advanced data management, and analytics models, and novel visualization methods, to create win-win-win solutions that improve urban environment, life quality, and city operation systems. This talk presents the vision and framework of urban computing, introducing the challenges and the state-of-the-art solutions in each layer of the framework. Based on the vision of urban computing, we have built an intelligent city operation system which has been deployed in over 20 cities as a digital foundation to empower Big Data-driven applications, such as logistic optimizations, traffic/crowd flow predictions, community demand and supply predictions, hazardous chemical management, and public resource allocations.

Bio

Dr. Yu Zheng is the Vice President of JD.COM and head JD Intelligent Cities Research. Before Joining JD.COM, he was a senior research manager at Microsoft Research. He currently serves as the Editor-in-Chief of ACM Transactions on Intelligent Systems and Technology and has served as the program co-chair of ICDE 2014 (Industrial Track), CIKM 2017 (Industrial Track) and IJCAI 2019 (industrial track). He is also a keynote speaker of AAAI 2019, KDD 2019 Plenary Keynote Panel and IJCAI 2019 Industrial Days. His monograph, entitled Urban Computing, has been used as the first text book in this field. In 2013, he was named one of the Top Innovators under 35 by MIT Technology Review (TR35) and featured by Time Magazine for his research on urban computing. In 2016, Zheng was named an ACM Distinguished Scientist and elevated to an IEEE Fellow in 2020 for his contributions to spatio-temporal data mining and urban computing.