Ann B Lee
Professor, Co-Director of PhD Program in Statistics
Department of Statistics & Data Science / Machine Learning Department, Carnegie Mellon University
Welcome!
I am a professor in the Department of Statistics & Data Science at Carnegie Mellon University, with a joint appointment in the Machine Learning Department. Prior to joining CMU in 2005, I was the J.W. Gibbs Assistant Professor in the department of mathematics at Yale University, and before that I served a year as a visiting research associate at the department of applied mathematics at Brown University.
My research interests are in developing statistical methodology for the type of complex data and problems often encountered in the physical sciences. I am particularly interested in statistical methods that adapt to nonlinear sparse structure in high-dimensional data, and nonparametric approaches that can handle heterogeneous data from different scientific probes. My recent work includes uncertainty quantification via conditional density estimation, likelihood-free inference, validation of emulator models, and applications in astronomy and hurricane intensity guidance involving satellite imagery and massive astronomical surveys.
In 2018, I started the STAtistical Methods for Physical Sciences (STAMPS) research group together with Mikael Kuusela. STAMPS is hosting public colloquia-style webinars open to all members of the scientific community, in addition to weekly research group meetings for students and faculty at CMU and UPitt. I am also key personnel in the recently founded NSF AI Planning Institute for Data-Driven Discovery in Physics.
Interests
- Statistical Machine Learning
- High-Dimensional Inference
- Nonlinear Dimension Reduction
- Statistics for the Physical Sciences
Education
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PhD in Physics
Brown University
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MSc/BSc in Engineering Physics
Chalmers University of Technology, Sweden
