Xiang Meng is a Post-Doc at Dana-Faber Cancer Institute, working with Hajime Uno, with an affiliation at Harvard T.H. Chan School of Public Health. He obtained his PhD from the Department of Statistics at Harvard University. He completed his PhD candidate in the Department of Statistics at Harvard University, with Luke Miratrix and Iavor Bojinov on causal inference.

Motivated by real-world contexts, his research develops causal inference methodologies designed for high-resolution data—information structures with granular detail that traditional statistical methods often simplify or ignore. Through statistical innovations that maintain validity while improving efficiency, his methods advance causal inference across healthcare delivery, business decision-making, and policy evaluation contexts. His interdisciplinary approach has been recognized through his selection as a Rising Star in Data Science.

He completed an internship with the Clinical Modeling & Evidence Integration team at Sanofi. Prior to Harvard, Xiang earned his Master of Science in statistics from the University of Washington. At UW, he worked with Prof. Thomas Richardson on coherent modelling of causal effects. He holds a Bachelor of Science in Quantitative Finance from National University of Singapore (2018).