I am a postdoctoral researcher in the Kavli Institute for Cosmology Cambridge and the Institute of Astronomy at the University of Cambridge. My research focuses on problems at the interface of astrophysics, statistics, machine learning, and computing (very often, of the high-performance variety). I am also very passionate about pedagogy and outreach. I am currently playing a major role in the construction and delivery of a large corporate data science training course contracted between the Cambridge Department of Applied Mathematics and Theoretical Physics and a large corporate partner.
Prior to Cambridge, 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 Group and Team Lead of the forecasting development and evaluation initiative. Under the supervision of Prof. Ryan Tibshirani, my team devoted our work to developing statistical models for forecasting COVID-19 incidence in the United States in order to support and advise the Centers for Disease Control and Prevention’s COVID-19 forecasting effort and the broader national response to the pandemic. I also held a guest researcher appointment at the Flatiron Institute's Center for Computational Astrophysics in New York. During my time in this role, I was fortunate to have the flexibility needed to prioritize my health and find the necessary treatment for a chronic pain condition that had plagued me since my days as an undergraduate.
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
Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe
Mapping the Large-Scale Universe through Intergalactic Silhouettes
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US
Flight of the Bumblebee: the Early Excess Flux of Type Ia Supernova 2023bee revealed by TESS, Swift and Young Supernova Experiment Observations
Photometric and Spectroscopic Analysis of SN 2022oqm: Closing the Gap Between SNe-Iax and Ic-like Calcium-Rich Transients
[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).