Hao ChenProfessor, Department of Statistics
Research Another major theme is high-dimensional similarity geometry: I study how conventional approaches may miss substantial signals, induce misleading structural patterns, or fail to reliably assess comparability. This perspective informs my work on latent structure discovery and statistical learning, including community detection, clustering, classification, and anomaly detection, as well as causal design. My methods are broadly applicable, with applications including neural recordings, wearable sensing and human movement, biological data, networked systems, and potential relevance to reliability and monitoring in modern AI systems. I welcome collaborations with statisticians, domain scientists, and industry researchers, especially on problems involving high-dimensional or complex data, modern scientific applications, data monitoring, system reliability, and AI-related settings. I also welcome inquiries from prospective students interested in related statistical methods or applications. Selected Honors PhD Students and Alumni Lynna Chu(Assistant Professor, Department of Statistics, Iowa State University) Hoseung Song(Postdoctoral Fellow, Fred Hutchinson Cancer Center → Assistant Professor, Department of Industrial and Systems Engineering, KAIST) Yi-Wei Liu(Data Scientist - Research, Google) Doudou Zhou(Postdoctoral Fellow, Harvard T.H. Chan School of Public Health → Assistant Professor, Department of Statistics & Data Science, National University of Singapore) Yuxuan Zhang(Financial Engineer, ChinaBond) Xiancheng Lin(Machine Learning Engineer, Uber) Yejiong Zhu(Data Scientist, Autodesk) Jing Lyu(Statistician, Abbott) Xiangbo Mo Mingshuo Liu(co-advised with Miles Lopes; Postdoctoral Fellow, Stanford University)
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