Yuling Yan

Yuling Yan is an Assistant Professor in the Department of Statistics at the University of Wisconsin-Madison (since August 2024), affiliated with the School of Computer, Data & Information Sciences. She holds a Ph.D. from Princeton University (2023) and a B.S. from Peking University (2018). Her research focuses on statistics, optimization, and data science applications to AI, social sciences, and reinforcement learning. Notable contributions include advancements in generative models, low-rank inference, and peer review mechanisms. She has been awarded the IMS Lawrence D. Brown Award (2024) and the Norbert Wiener Postdoctoral Fellowship (2023).

Educational Background:

• Bachelor of Science in Mathematics, Peking University (2014–2018)
• Ph.D. in Operations Research & Financial Engineering, Princeton University (2018–2023)

Research Interests:

Yuling’s work bridges statistical theory and machine learning applications, with emphasis on generative AI, causal inference, and algorithmic fairness. Key areas include:

• Learning in probability measure spaces for generative models
• Uncertainty quantification in low-rank models
• Optimization strategies in reinforcement learning
• Peer review mechanisms in machine learning conferences

Teaching:

She teaches STAT 615: Statistical Learning at UW-Madison, focusing on theoretical foundations of prediction and decision-making models, including classification, regression, and unsupervised learning. Her courses emphasize mathematical rigor using tools like concentration inequalities and Hilbert spaces.

Labs/Teams: Her research group explores interdisciplinary projects in generative AI, causal inference, and reinforcement learning, often collaborating with institutions like MIT and Berkeley.