Ming Yuan

Ming Yuan is a Professor in the Department of Statistics at Columbia University and serves as Associate Director of the Data Science Institute. His research focuses on high-dimensional statistics, machine learning, and statistical methodology with applications in genomics, finance, and imaging. Yuan holds a Ph.D. in Statistics from the University of Wisconsin-Madison (2004) and a B.S. in Electrical Engineering from the University of Science and Technology of China (1997).

Education:

• 2004 Ph.D., Statistics, University of Wisconsin-Madison
• 2003 M.S., Computer Science, University of Wisconsin-Madison
• 2000 M.S., Probability and Statistics, University of Science and Technology of China
• 1997 B.S., Electrical Engineering, University of Science and Technology of China

Research Interests: Dr. Yuan’s work bridges theoretical and applied statistics, emphasizing scalable methods for high-dimensional data. Key areas include tensor decomposition, covariance estimation, and statistical machine learning. His contributions to methods like sparse inverse covariance estimation and matrix/tensor completion have found applications in finance, genomics, and image analysis.

Publications: His recent work explores tensor-based methods for high-dimensional analysis and develops optimal algorithms for compressed sensing. Articles often address statistical theory and computational challenges in modern data science, reflecting a balance between foundational and applied research.

Awards:

• 2025 JASA Theory & Method Invited Discussion Paper
• 2024 William F. Sharpe Award (JFQA)
• 2018 Medallion Lecturer (Institute of Mathematical Statistics)
• 2014 Guy Medal in Bronze (Royal Statistical Society)
• 2007 Leo Breiman Junior Award

Professional Activities: Yuan has served as Co-Editor of The Annals of Statistics (2019–2021) and Program Secretary for the Institute of Mathematical Statistics (2018–2021). His work integrates interdisciplinary collaborations, particularly in biomedical imaging and financial econometrics.