Hi, I'm an assistant Professor at New York University Shanghai. I earned my Ph.D. in Industrial Engineering and Management Sciences from Northwestern University in 2025, and my B.S. and B.A. from Peking University in 2018.

I'm broadly interested in machine learning and AI, including (a) the responsible use of AI and learning algorithms (e.g., fairness, collusion), (b) understanding and managing the risk of generative AI (e.g., exploratory interaction, adversarial competitors, and privacy preservation), and (c) applications to healthcare, revenue management, and supply chain management.

My research has two streams:
(i) Optimizing Queues with Low Traffic Intensity: This stream focuses on analyzing service systems with low arrival and service rates. We developed a novel finite approximation method to accurately evaluate and optimize the performance of such systems. One application is the management of transplant programs, where organ allocation rates are inherently low.
(ii) Multi-Agent Decision-Making with Incomplete Information: This stream examines systems with multiple decision-makers and incomplete information. We developed a multi-agent gradient framework to analyze the system dynamics and convergence. This framework has broad applicability; e.g., in revenue management contexts, a competitive market with multiple sellers and uncertain demand.

I’m actively seeking faculty and student collaborators! If you share any research interests, I’d be happy to connect—feel free to reach out! 

I am also recruiting postdoctoral researchers, Ph.D. students, and student RAs with my coauthors. Please feel free to contact me if you are interested.

Contact: shukaili2024 [at] u.northwestern.edu