AI for Complex Problems in Smart City

In smart city applications, there are many real-life issues, some being simple and some being complex. In those that are considered complex and hard, algorithmic solutions usually do not work well. Deep learning techniques may help, but careful design of AI models is needed. In this talk, we will cover three sectors about smart cities: (i) smart transportation, (ii) smart mobility, and (iii) precision sports. We will address how we tackle several complex issues in smart cities, including trajectory embedding, field data inpainting, self-occluded pose analysis, 24-hour level crossing detection, boundary localization, and 6G for driving safety. Our trials and findings on these issues will be shared with the audience.

Yu-Chee Tseng received his Ph.D. in Computer and Information Science from the Ohio State University in January of 1994. He served as Chairman (2005-2009) and as Dean (2011-2017), College of Computer Science, National Yang Ming Chiao Tung University (NYCU), Taiwan. He is founding Dean of College of AI, NYCU. Currently, he is Director of Pervasive AI Research Labs, NYCU.
Dr. Tseng has been awarded as NCTU Chair Professor (2011-present) and Y. Z. Hsu Scientific Chair Professor (2012-2013). He received Outstanding Research Award (National Science Council, 2001, 2003, and 2009), Academic Award (Ministry of Education), Best Paper Awards (ICPP 2003, iThings 2014, APNOMS 2015, and IoTaaS 2017), Elite I. T. Award (2004), and Distinguished Alumnus Award (Ohio State University, 2005), Y. Z. Hsu Scientific Paper Award (2009), TWAS Prize (2018), and National Chair Professorship (2020-2023). His research interests include mobile computing, wireless communication, IoT, and AI. Dr. Tseng is an IEEE Fellow. He served on the editorial boards of IEEE Trans. on Vehicular Technology, IEEE Trans. on Mobile Computing, IEEE Trans. on Parallel and Distributed Systems, and IEEE Internet of Things Journal. His citation h-index is 68.