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”.
đźš© Upcoming! May 29-June 19, 2022: Workshop “Interplay of Fundamental Physics and Machine Learning”, Aspen Center of Physics. (Co-organized with Konstantin Matchev, Harrison Prosper, and Jesse Thaler)
PhD in Physics
Brown University
MSc/BSc in Engineering Physics
Chalmers University of Technology, Sweden
I coordinate the STAtistical Methods for the Physical Sciences (STAMPS) Research Group at CMU together with Mikael Kuusela.
I am fortunate to advise the following amazing students:
Current PhD Students
Trey McNeely (thesis) | David Zhao (thesis) | Luca Masserano | ![]() |
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Galen Vincent | Woonyoung Chang (ADA project 2022) | ![]() |
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Previous PhD Students
Niccolò (Nic) Dalmasso
– PhD May 2021, Department of Statistics & Data Science, CMU
– Thesis title: Uncertainty Quantification in Simulation-based Inference
– 2021 ASA Student of the Year, Pittsburgh Chapter
Taylor Pospisil
– PhD May 2019, Department of Statistics & Data Science, CMU
– Thesis title: Conditional Density Estimation for Regression and Likelihood-Free Inference
Rafael Izbicki
– PhD April 2014, Department of Statistics, CMU
– Thesis title: A Spectral Series Approach to High-Dimensional Nonparametric Inference
– 2014 Best Thesis Award, Department of Statistics, CMU
Di Liu
– PhD July 2012, Department of Statistics, CMU
– Thesis title: Comparing Data Sources in High Dimensions
Andrew Crossett
– co-advised with Kathryn Roeder
– PhD May 2012, Department of Statistics, CMU
– Thesis title: Using Dimension Reduction Techniques to Model Genetic Relationships for Association Studies
Susan Buchman
– co-advised with Chad Schafer
– PhD March 2011, Department of Statistics, CMU
– Thesis title: High-Dimensional Adaptive Basis Density Estimation
Joseph W. Richards
– co-advised with Chad Schafer
– PhD July 2010, Department of Statistics, CMU
– Thesis title: Fast and Accurate Estimation for Astrophysical Problems in Large Databases
– 2010 ASA Student of the Year, Pittsburgh Chapter
Diana Luca
– co-advised with Kathryn Roeder
– PhD Sept 2008, Department of Statistics, CMU
– Thesis title: Genetic Matching by Ancestry in Genome-Wide Association Studies
(non-technical)
“Statistical Inference for Complex Data in the Physical Sciences”, PhD Open House, Department of Statistics & Data Science, CMU, March 2020.
“Statistics and Machine Learning for the Physical Sciences”, Public Lecture Series, Allegheny Observatory, November 2019.