Jiayi Wang

Jiayi Wang is an Assistant Professor of Statistics in the Department of Mathematical Sciences at the University of Texas at Dallas (UT Dallas), affiliated with the Erik Jonsson School of Engineering and Computer Science. He holds a PhD in Statistics from Texas A&M University (2022) and a B.S. in Statistics from Zhejiang University (2017). His research focuses on nonparametric statistics and machine learning, particularly in causal inference, functional data analysis, reinforcement learning, low-rank modeling, and matrix completion. He has contributed to methodological advancements in areas such as treatment effect estimation, offline reinforcement learning, and statistical theory for complex data structures.

Key achievements include the ASA Section on Nonparametric Statistics Student Paper Award (2020). His work has been published in prestigious journals like the Journal of the American Statistical Association and conferences such as NeurIPS and ICML. He has also developed open-source code for methods like PCATE balancing weights, available on GitHub.

Teaching experience includes instructing courses in statistical learning, probability, and applied statistics at both Texas A&M University and UT Dallas. His research group actively explores interdisciplinary applications, including climate science and criminal justice, demonstrating a commitment to bridging statistical theory with real-world problems.