About Me
In July of 2022, I will join the Institute of Astronomy and the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge, where I will continue to work on problems at the interface of astrophysics, statistics, and machine learning as a Postdoctoral Research Associate under the supervision of Professor 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. Our 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.
Publications
Three-dimensional cosmography of the high redshift Universe using intergalactic absorption
Collin A. Politsch, Jessi Cisewski-Kehe, Rupert A.C. Croft, and Larry Wasserman
Pre-submission Inquiry approved by Nature.
Webinar Talk (YouTube)
Trend Filtering - I. A Modern Statistical Tool for Time-Domain Astronomy and Astronomical Spectroscopy
Collin A. Politsch, Jessi Cisewski-Kehe, Rupert A.C. Croft, and Larry Wasserman
Monthly Notices of the Royal Astronomical Society, 492(3), March 2020, p. 4005-4018.
Best paper finalist in the 2020 ASA Astrostatistics Student Paper Competition. [Finalists]
Publisher
arXiv
trendfiltering R package
Trend Filtering - II. Denoising Astronomical Signals with Varying Degrees of Smoothness
Collin A. Politsch, Jessi Cisewski-Kehe, Rupert A.C. Croft, and Larry Wasserman
Monthly Notices of the Royal Astronomical Society, 492(3), March 2020, p. 4019-4032.
Best paper finalist in the 2020 ASA Astrostatistics Student Paper Competition. [Finalists]
Publisher
arXiv
trendfiltering R package
Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US
Estee Y Cramer, Evan L Ray, Velma K Lopez, et al.
Proceedings of the National Academy of Sciences, Volume 119, Issue 15, April 2022.
A collaboration between the Reich Lab, the Delphi Research Group, the Centers for Disease Control and Prevention, and others.
Publisher
medRxiv
COVID-19 Forecast Hub
An Open Repository of Real-Time COVID-19 Indicators
Alex Reinhart, Logan Brooks, Maria Jahja, Aaron Rumack, Jingjing Tang, and the Delphi Research Group [58 more authors]
Proceedings of the National Academy of Sciences, 118(51), December 2021.
Publisher
medRxiv
Supplement
Data access
Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe
Collin A. Politsch
Carnegie Mellon University, Ph.D. Dissertation, June 2020.
2020-'21 Winner of the Umesh K. Gavaskar Memorial Award for the Best Ph.D. Dissertation in Statistics and Data Science at Carnegie Mellon University.
CMU Archive
Mapping the Large-Scale Universe through Intergalactic Silhouettes
Collin A. Politsch and Rupert A.C. Croft
CHANCE, 32(3), Sept. 2019, p. 14-19.
Publisher
Augmenting Adjusted Plus-Minus in Soccer with FIFA Ratings
Francesca Matano, Lee F. Richardson, Taylor Pospisil, Collin A. Politsch, and Jining Qin
Submitted to the Journal of Quantitative Analysis in Sports.
arXiv
Analytics Website
News
Postdoc Position at Cambridge
Starting July 2022
In July of 2022, I will join the Institute of Astronomy and the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge, where I will continue to work at the interface of astrophysics, statistics, and machine learning as a Postdoctoral Research Associate under the supervision of Prof. Kaisey Mandel.
Paper published in PNAS
04/2022
A co-authored manuscript "Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the United States" (a collaboration between the Reich Lab, the Delphi Research Group, the Centers for Disease Control and Prevention, and others) was published in Proceedings of the National Academy of Sciences.
[Publisher] [medRxiv] [COVID-19 Forecast Hub]
[Publisher] [medRxiv] [COVID-19 Forecast Hub]
Paper published in PNAS
12/2021
A co-authored manuscript "An Open Repository of Real-Time COVID-19 Indicators" by the Delphi Research Group (lead authors: Alex Reinhart, Logan Brooks, Maria Jahja, Aaron Rumack, Jingjing Tang) was published in Proceedings of the National Academy of Sciences.
[Publisher] [medRxiv] [Supplement] [Data Access]
[Publisher] [medRxiv] [Supplement] [Data Access]
JSM session on Cosmostatistics
08/2021
I organized a JSM 2021 session Statistical Challenges in Cosmology, in which some statistician-cosmologists, cosmologist-statisticians, and I gave an overview of this growing nexus field and some of our respective research within it.
Guest Researcher at CCA, Flatiron Institute
07/2021
I was fortunate to be given a Guest Researcher position at the Flatiron Institute's Center for Computational Astrophysics in NYC.
Award for Best Ph.D. Dissertation
05/2021
My Ph.D. dissertation "Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe" 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 University. [Dissertation]
Preprint submitted to JQAS
01/2021
A co-authored manuscript "Augmenting Adjusted Plus-Minus in Soccer with FIFA Ratings" was submitted to the Journal of Quantitative Analysis in Sports (lead authors: Francesca Matano and Lee F. Richardson). [arXiv] [Player Rankings]
Postdoc Fellowship in the CMU Machine Learning Department
08/2020
I accepted an offer to join the Machine Learning Department at Carnegie Mellon University for a 1-year postdoctoral fellowship under the supervision of Ryan Tibshirani, during which I will be a core member of the CMU-based Delphi Research Group. My research will focus on developing statistical models for forecasting COVID-19 incidence in the United States to help inform a data-driven national response to the COVID-19 pandemic and future threats to public health.
Ph.D. Defense
06/2020
I successfully defended my dissertation "Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe", earning a Joint Ph.D. in Statistics and Machine Learning from Carnegie Mellon University. [Dissertation]
Paper published in MNRAS
03/2020
Paper published in MNRAS
03/2020
Paper Award
01/2020
My manuscript "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.
Finalists: Josh Speagle (Harvard), Collin Politsch (CMU), Matt Ho (CMU), Oliver Philcox (Princeton), Richard Feder (Caltech).
Finalists: Josh Speagle (Harvard), Collin Politsch (CMU), Matt Ho (CMU), Oliver Philcox (Princeton), Richard Feder (Caltech).
Article published in CHANCE magazine
09/2019
I wrote an article, titled "Mapping the Large-Scale Universe through Intergalactic Silhouettes", for CHANCE magazine's special issue on astrostatistics. [Article]
Surname change
03/2019
I changed my surname to Politsch to honor my late mother, Carol Politsch.
Hackathon podium
10/2018
Three of my classmates and I took 2nd in The Data Open at CMU hosted by Citadel and Correlation One (300+ applications, ~125 selected to compete).
Internship at Uber Headquarters
06/2018 - 08/2018
I interned as a data scientist at Uber Headquarters in San Francisco during the summer of 2018. While there, I completed an end-to-end project which culminated in a new personalized ranking and recommendation algorithm for the Uber Eats iOS/Android home feed. The algorithm showed significant improvement in both offline evaluation and online A/B testing over their state-of-the-art algorithm used in the app to that point and was subsequently launched as its replacement.
Hackathon press release
11/2017
Carnegie Mellon detailed some of our hackathon successes in the following press release:
CMU Statistics and Data Science Graduate Students Keep Winning Big.
CMU Statistics and Data Science Graduate Students Keep Winning Big.
Hackathon podium
09/2017
Three of my classmates and I took 2nd place in the 2017 NBA Hackathon (900+ applications, ~200 selected to compete). [Announcement] [Recap]
Hackathon podium
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).
Hackathon podium
05/2017
Three of my classmates and I took 3rd place in the second annual Google BrainHub Neurohackathon, hosted by the Machine Learning Department at Carnegie Mellon University. [Press Release] [Live Coverage]
Contact
Press Inquiries / Collaborations / General Questions
Images
Some Snapshots from my Journey
