Rui Miao

Rui Miao serves as an Assistant Professor in the Department of Mathematical Sciences within the School of Natural Sciences and Mathematics at The University of Texas at Dallas. Previously, he worked as a Mathematical Statistician at the Office of Biostatistics Research at NIH/NHLBI and completed postdoctoral training under supervision of Dr. Annie Qu and Dr. Babak Shahbaba. His academic foundation includes a PhD in Statistics from The George Washington University under Dr. Xiaoke Zhang's mentorship.

Education:

• PhD in Statistics, The George Washington University

Dr. Miao's research program bridges advanced statistical methodology with critical healthcare applications. His work focuses on Reinforcement Learning for personalized treatment policies, Causal Inference methods addressing unmeasured confounding, Health AI applications, Functional Data Analysis techniques, and computational approaches to Few-shot Learning. His methodological innovations particularly address challenges in heterogeneous medical data and complex decision frameworks.

Analyzing his publication trajectory reveals a strong emphasis on developing statistical frameworks for personalized medicine, with increasing interdisciplinary collaboration in cardiology, immunology, and neuroscience. His work spans theoretical statistics in journals like Annals of Statistics and Journal of the American Statistical Association to applied medical research in Science Advances and Journal of the American College of Cardiology.

Scientific Recognition:

• 2021 ICSA Student Paper Award for work on wavelet-based independence testing

Dr. Miao actively contributes to the academic community through invited talks at institutions including National Cancer Institute, Duke University, and NIH, presenting on reinforcement learning under heterogeneity and functional data analysis methods. His teaching portfolio at UT Dallas includes STAT 5304 Introduction to Human Health Research, building on previous teaching experience at The George Washington University covering statistical theory and applied courses.