His main research work lies in artificial intelligence (AI) and machine learning. Notably, it focuses on adaptive control (reinforcement learning, Markov decision process), multi-objective optimization (compromise programming, fair optimization), and preference handling (representation, elicitation, and learning). 

His teaching interests at Duke Kunshan include AI, machine learning, algorithms.

Before joining academia, he was a financial quantitative analyst in London, UK. As a researcher, he regularly publishes in top AI and machine learning venues (e.g., IJCAI, AAAI, ICML…). He has served as an area chair at AAAI and ECAI. Several of his papers received a best paper award (e.g., MIWAI, ALA). His work has been funded both by public funding agencies (NSFC, Shanghai NSF) and private companies (Yahoo, Huawei, Netease).

Paul Weng received a French engineer degree in statistics from ENSAI (National School of Statistics and Information Analysis), Bruz, France. He has a M.S. and Ph.D. in AI from Sorbonne Université, Paris, France. He was a regular or visiting faculty member of many universities (Sorbonne Université, Carnegie Mellon University, Sun Yat-sen University, Shanghai Jiao Tong University, University of Nottingham Ningbo China). Before joining Duke Kunshan, he was an associate professor at the University of Michigan-Shanghai Jiao Tong University Joint Institute.