I am a postdoctoral research associate in the Institute of Astronomy and the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge, where I work on problems at the interface of astrophysics, statistics, and machine learning under the supervision of Prof. Kaisey Mandel.
During the 2020-'21 academic year, I was a postdoctoral fellow in the Machine Learning Department at Carnegie Mellon University, where I was a core member of the CMU-based Delphi Research Group and Lead of the forecasting development and evaluation team. My team's research was devoted to developing statistical models for forecasting COVID-19 incidence in the United States in order to help inform a data-driven national response to the COVID-19 pandemic.
I earned a Joint Ph.D. in Statistics and Machine Learning from Carnegie Mellon University in 2020 under the multidisciplinary supervision of Professors Larry Wasserman, Jessi Cisewski-Kehe, and Rupert Croft. My dissertation "Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe" was devoted to a variety of problems in astrostatistics and astroinformatics, and was selected (by faculty vote) as the 2020-'21 winner of the Umesh K. Gavaskar Memorial Award for the Best Ph.D. Dissertation in Statistics and Data Science at Carnegie Mellon. Prior to earning my Ph.D., I received an M.Sc. in Machine Learning from Carnegie Mellon and a B.Sc. in Mathematics from the University of Kansas.
Outside of my work, I enjoy athletics, reading, traveling, any food wrapped in a tortilla, and everything about parenting my angelic golden retriever, Maximus.
My name is pronounced kä•lən pō•lich.
Three-dimensional cosmography of the high redshift Universe using intergalactic absorption
Trend Filtering - I. A Modern Statistical Tool for Time-Domain Astronomy and Astronomical Spectroscopy
Trend Filtering - II. Denoising Astronomical Signals with Varying Degrees of Smoothness
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US
Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe
Mapping the Large-Scale Universe through Intergalactic Silhouettes
[Publisher] [medRxiv] [COVID-19 Forecast Hub]
[Publisher] [medRxiv] [Supplement] [Data Access]
Finalists: Josh Speagle (Harvard), Collin Politsch (CMU), Matt Ho (CMU), Oliver Philcox (Princeton), Richard Feder (Caltech).
CMU Statistics and Data Science Graduate Students Keep Winning Big.