Report Title: Understanding and Advancing Foundation Models

Speaker: Assistant Professor Jindong Wang

Affiliation: College of William & Mary, USA

Time: 15:30, Monday, June 23, 2025

Venue: Meeting Room 706, Block A, Science and Education Building

Report Abstract:

In the era of generative artificial intelligence, large foundation models play a central role with their advanced understanding and generation capabilities. However, they also face numerous significant limitations. These limitations include challenges in coping with the unpredictable real world, such as out-of-distribution data, noisy inputs and security issues. Secondly, given their profound impact on society, we must promote interdisciplinary cooperation to comprehensively evaluate their potential benefits and risks, deepen the understanding of human-computer interaction, and advocate the responsible application of artificial intelligence. In this talk, I will share my latest research, insights and future plans in these key areas, explore how to give full play to the potential of large foundation models while addressing the existing limitations, and realize the responsible integration of artificial intelligence in the context of rapid development.

Speaker Profile:

Dr. Jindong Wang is a Tenure-Track Assistant Professor in the Department of Data Science at the College of William & Mary. He served as a Senior Researcher at Microsoft Research Asia from 2019 to 2024. His research interests include machine learning, large language and foundation models, and the application of artificial intelligence in social sciences. He is named one of the world's top 2% highly cited scientists by Stanford University (with a total of more than 19,000 citations and an H-index of 49). He currently serves as an Associate Editor of IEEE TNNLS, a Guest Editor of ACM TIST, a Area Chair of ICML, NeurIPS, ICLR, KDD, ACL, ACMMM and ACML, and a Senior Program Committee Member of IJCAI and AAAI. His research results have been reported by international media such as Forbes and MIT Technology Review. He received his Ph.D. from the University of the Chinese Academy of Sciences in 2019 and won the Excellent Doctoral Dissertation Award, and his Bachelor's degree from North China University of Technology in 2014.