Collin A. Politsch, Ph.D.
Postdoctoral Fellow
Machine Learning Department
Carnegie Mellon University
About Me
I am a postdoctoral fellow in the Machine Learning Department at Carnegie Mellon University, where I serve as the lead of COVID-19 forecast development on the Delphi pandemic response team under the supervision of Ryan Tibshirani.
I completed my Ph.D. at CMU in the summer of 2020, jointly studying in the Department of Statistics & Data Science and the Machine Learning Department. My dissertation focused on a variety of problems at the interface of statistics, machine learning, and astrophysics, and was supervised by Larry Wasserman, Jessi Cisewski-Kehe, and Rupert Croft. I previously received an M.Sc. in machine learning from CMU and a B.Sc. in mathematics from the University of Kansas.
In my free time I enjoy athletics, reading, traveling, any food wrapped in a tortilla, and my dogs -- Seth and Max. I changed my last name from Eubanks to Politsch in March of 2019 in honor of my late mother, Carol Politsch.Publications and technical reports
Three-dimensional cosmography of the high redshift Universe using intergalactic absorption
Collin A. Politsch, Jessi Cisewski-Kehe, Rupert A.C. Croft, and Larry Wasserman
In preparation, Inquiry approved by NatureTrend Filtering - I. A Modern Statistical Tool for Time-Domain Astronomy and Astronomical Spectroscopy [Link] [arXiv] [GitHub]
Collin A. Politsch, Jessi Cisewski-Kehe, Rupert A.C. Croft, and Larry Wasserman
Monthly Notices of the Royal Astronomical Society, 492(3), p. 4005-4018, 2020, DOI: 10.1093/mnras/staa106-
Trend Filtering - II. Denoising Astronomical Signals with Varying Degrees of Smoothness [Link] [arXiv] [GitHub]
Collin A. Politsch, Jessi Cisewski-Kehe, Rupert A.C. Croft, and Larry Wasserman
Monthly Notices of the Royal Astronomical Society, 492(3), p. 4019-4032, 2020, DOI: 10.1093/mnras/staa110 -
Mapping the Large-Scale Universe through Intergalactic Silhouettes [Link]
Collin A. Politsch and Rupert A.C. Croft
CHANCE, 32(3), 14-19, 2019, DOI: 10.1080/09332480.2019.1662696 -
Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe [Link]
Collin A. Politsch. Ph.D. Dissertation (supervised by Larry Wasserman, Jessi Cisewski-Kehe, and Rupert A.C. Croft) -
Multi-resolution Regression, Divide and Conquer Risk Estimation, and the Large-scale Universe [Link] [Slides]
Collin A. Politsch. Ph.D. Dissertation Proposal (supervised by Larry Wasserman, Jessi Cisewski-Kehe, and Rupert A.C. Croft) -
Statistical Methods for Estimating Regression Quantiles [Link]
Collin A. Politsch, Purvasha Chakravarti, and Jining Qin. -
Augmenting Adjusted Plus-Minus in Soccer with FIFA Ratings [arXiv] [Website]
Francesca Matano, Lee F. Richardson, Taylor Pospisil, Collin Eubanks, and Jining Qin
Preprint. arXiv:1810.08032. http://intraocular.net
Finalist for best paper in the ASA Astrostatistics Student Paper Competition, sponsored by the Astrostatistics Interest Group. Awarded at JSM in August 2020.
Finalist for best paper in the ASA Astrostatistics Student Paper Competition, sponsored by the Astrostatistics Interest Group. Awarded at JSM in August 2020.
News
08/2020 - I joined the Machine Learning Department at Carnegie Mellon as a Postdoctoral Fellow supervised by Ryan Tibshirani.
06/2020 - I successfully defended my dissertation: Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe. [Document]
03/2020 - Trend Filtering - II. Denoising Astronomical Signals with Varying Degrees of Smoothness published in Monthly Notices of the Royal Astronomical Society. [Link]
03/2020 - Trend Filtering - I. A Modern Statistical Tool for Time-Domain Astronomy and Astronomical Spectroscopy published in Monthly Notices of the Royal Astronomical Society. [Link]
01/2020 - My paper Trend Filtering: A Modern Statistical Tool for Time-Domain Astronomy and Astronomical Spectroscopy was selected as a finalist for the best paper in the ASA Astrostatistics Student Paper Competition, sponsored by the Astrostatistics Interest Group.
09/2019 - CHANCE article: Mapping the Large-Scale Universe through Intergalactic Silhouettes [Link]
10/2018 - Joint work on Augmenting Adjusted Plus-Minus in Soccer with FIFA Ratings is now online (Lead authors: Francesca Matano and Lee F. Richardson) [arXiv:1810.08032, intraocular.net]
10/2018 - Three of my classmates and I took 2nd place in The Data Open at CMU hosted by Citadel and Correlation One (300+ applications, ~125 selected to compete).
05/2018 - I will be interning as a data scientist at Uber HQ in San Francisco during the summer of 2018.
11/2017 - Media: CMU Statistics and Data Science Graduate Students Keep Winning Big (article detailing hackathon success)
09/2017 - Three of my classmates and I took 2nd place in the 2017 NBA Hackathon (900+ applications, ~200 selected to compete). [Recap]
09/2017 - Three of my classmates and I took 2nd place in The Data Open at CMU hosted by Citadel and Correlation One (550+ applications, ~125 selected to compete).
05/2017 - I successfully defended my thesis proposal: Multi-resolution Regression, Divide and Conquer Risk Estimation, and the Large-scale Universe. [Document] [Slides]
05/2017 - Three of my classmates and I took 3rd place in the Google BrainHub Neurohackathon, hosted by the Machine Learning Department at CMU.