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 complex data and problems 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 at the NSF AI Planning Institute for Data-Driven Discovery in Physics. In July 2021, I co-organized our AI-physics virtual conference “From Quarks to Cosmos with AI”.
Interests
- Statistical Machine Learning
- High-Dimensional Inference
- Nonlinear Dimension Reduction
- Likelihood-Free Inference
- 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
