معرفی
Summary
Ann B. Lee is a Professor in the Department of Statistics & Data Science and the Machine Learning Department at Carnegie Mellon University (CMU). She serves as Co-Director of the Ph.D. Program in Statistics. Previously, she was the J.W. Gibbs Assistant Professor at Yale University and a visiting researcher at Brown University. Her research focuses on developing statistical methods for complex data in physical sciences, including trust-worthy uncertainty quantification, likelihood-free inference, and applications in astronomy, climate science, and hurricane dynamics.
Education: Ph.D. in Physics from Brown University (2002); M.Sc./B.Sc. in Engineering Physics from Chalmers University of Technology (Sweden).
Key Research Interests:
- Scientific Machine Learning
- Uncertainty Quantification (UQ)
- Likelihood-Free Inference
- Tropical Cyclone Analysis
- High-Dimensional Data Modeling
She leads the STAMPS (STAtistical Methods for Physical Sciences) research group, which bridges classical statistics and machine learning. Recent work includes methods for estimating ocean thermal responses to hurricanes, probabilistic forecasting, and diagnostics for generative models. Her team collaborates with climate and astrophysics communities, hosting public webinars and symposia.
Advising: Supervised over 15 PhD students, including graduates now in academia and industry. Current advisees include Luca Masserano and Alex Shen.
Labs/Teams: Co-directs the STAMPS Research Center at CMU, launching in Fall 2024 as a university-wide initiative.
