Yixin Wang

Yixin Wang is an Assistant Professor of Statistics at the University of Michigan, affiliated with the Department of Statistics within the College of Literature, Science, and the Arts. His research focuses on Bayesian statistics, causal inference, and machine learning. Previously, he was a postdoctoral researcher at UC Berkeley under Michael Jordan and earned his Ph.D. in Statistics from Columbia University (2020) and B.Sc. in Mathematics & Computer Science from Hong Kong University of Science and Technology (2014).

• Education: Ph.D., Columbia University (2020); B.Sc., HKUST (2014)

Research Interests:

• Probabilistic generative modeling and Bayesian statistics, including large language models and diffusion models
• Causal machine learning, including causal representation learning and causal inference for language models
• Applications in recommender systems, computational biology, and human-AI interactions

His work emphasizes robust statistical methods and causal approaches to address real-world challenges, such as bias mitigation in algorithms and equitable treatment allocation in healthcare. He currently leads a research group seeking to advance causal machine learning and probabilistic models, with postdoctoral openings available.

Key contributions include developing robust Bayesian methods, causal inference frameworks for unstructured data, and applications in electronic health records and materials discovery. He has been instrumental in bridging theory and practice in areas like feedback loop mitigation in recommenders and causal fairness assessment.