Speaker: Xing YAN 中国人民大学
Time: 2024-11-05 10:00-2024-11-05 11:30
Venue: FIT 1-222

Abstract:

AI in Finance is a fascinating cross-disciplinary area. However, a significant gap exists between these two fields, characterized by distinct cultures and objectives. In this presentation, our goal is to bridge this gap by highlighting the unique aspects of machine learning in finance and introducing novel methodologies with successful applications. We will cover three key topics: uncertainty quantification, generative learning, and model stability in finance. We contend that end-to-end black-box models are generally ineffective in this context. Instead, models elaborately designed to capture the natures of financial data, such as uncertainty, dependency structures, tail properties, and OOD, are more likely to succeed.

Short Bio:

Dr. Yan obtained his Ph.D. degree from Department of SEEM (financial engineering field), Chinese University of Hong Kong in 2019. He also received a master's degree in computer science from Institute of Computing Technology, Chinese Academy of Sciences, and a bachelor's degree in pure mathematics from Nankai University. Dr. Yan works at the intersection of machine learning and finance/business. His research papers have been published in journals/conferences in both two communities, such as IEEE TPAMI, IEEE TNNLS, European Journal of Operational Research, Journal of Economic Dynamics and Control, NeurIPS, AAAI, ECCV, ICAIF, etc.