معرفی
Chad M. Schafer is an Associate Professor in the Department of Statistics & Data Science at Carnegie Mellon University, specializing in statistical methodology for astronomy and cosmology. He co-chairs the LSST Informatics and Statistics Science Collaboration and is affiliated with the McWilliams Center for Cosmology at CMU. His research focuses on rigorous handling of complex models and high-dimensional data in the sciences, particularly astronomy.
- Ph.D. in Statistics, University of California, Berkeley (2004)
- M.S. in Statistics, University of Illinois at Urbana-Champaign
- B.S. in Statistics, Western Michigan University
- Former staff at Argonne National Laboratory (Mathematics and Computer Science Division)
His research spans topics such as likelihood-free inference, Bayesian computation, photometric redshift estimation, and semi-supervised learning for supernova classification. He has applied statistical methods to cosmological surveys like SDSS and LSST, as well as climate modeling and hurricane track analysis.
Recent publications highlight applications of statistical techniques to astrophysics, including Approximate Bayesian Computation for supernovae, SCA-based photometric redshift estimation, and high-dimensional density modeling. His work intersects astronomy, data science, and computational statistics.
He has served in multiple educational roles, including:
- Teaching data science courses for CMU's Master of Science in Computational Finance (MSCF) program
- Steering Committee member for MSCF
- Instructor for the Summer School in Statistics for Astronomers at Penn State's Center for Astrostatistics
- Moderator of the methodology subsection of the arXiv Statistics area (2007-2018)
- Director of CMU's Summer Undergraduate Research Experience in Statistics program (2015-2018)
His departmental affiliations and committee roles underscore his interdisciplinary approach, bridging statistical theory with practical applications in astronomy and finance.
