Collin A. Politsch, Ph.D.
Postdoctoral Fellow
Machine Learning Department
Carnegie Mellon University
Collin A. Politsch, Ph.D.
Postdoctoral Fellow
Machine Learning Department
Carnegie Mellon University
Now at https://collinpolitsch.com!
About Me
I am a Postdoctoral Fellow in the Machine Learning Department at Carnegie Mellon University, where I am a core member of the CMU-based Delphi Research Group. My research is currently 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 and future threats to public health.
In the 2021-'22 academic year, I will join the Institute of Astronomy and the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge, where I will resume research at the interface of astrophysics, statistics, and machine learning under the supervision of Kaisey Mandel.
I earned a joint Ph.D. in Statistics and Machine Learning from Carnegie Mellon University in 2020 under the multi-disciplinary supervision of 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 & 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.
Publications, Preprints, and Dissertation
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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. Pre-submission Inquiry approved by Nature. Trend 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), March 2020, Pages 4005-4018.Finalist for best paper in the ASA Astrostatistics Student Paper Competition, sponsored by the Astrostatistics Interest Group.
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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), March 2020, Pages 4019-4032. -
An Open Repository of Real-Time COVID-19 Indicators
Alex Reinhart, Logan Brooks, Maria Jahja, Aaron Rumack, Jingjing Tang, Wael Al Saeed, Taylor Arnold, Amartya Basu, Jacob Bien, Angel A. Cabrera, Andrew Chin, Eu Jing Chua, Brian Clark, Nat DeFries, Jodi ´ Forlizzi, Samuel Gratz, Alden Green, George Haff, Robin Han, Addison J. Hu, Sangwon Hyun, Ananya Joshi, Jimi Kim, Andrew Kuznetsov, Wichada La Motte-Kerr, Yeon Jin Lee, Kenneth Lee, Zachary C. Lipton, Michael X. Liu, Lester Mackey, Kathryn Mazaitis, Daniel J. McDonald, Balasubramanian Narasimhan, Natalia L. Oliveira, Pratik Patil, Adam Pereri, Collin A. Politsch, Samyak Rajanala, Dawn Rucker, Nigam H. Shah, Vishnu Shankar, James Sharpnack, Dmitry Shemetov, Noah Simon, Vishakha Srivastava, Shuyi Tan, Robert Tibshirani, Elena Tuzhilina, Ana Karina Van Nortwick, Valerie Ventura, Larry Wasserman, Jeremy C. Weiss, Kristin Williams, Roni Rosenfeld, and Ryan J. Tibshirani
Submitted to Proceedings of the National Academy of Sciences. -
Mapping the Large-Scale Universe through Intergalactic Silhouettes
[Link]
Collin A. Politsch and Rupert A.C. Croft
CHANCE, 32(3), September 2019, Pages 14-19. -
Augmenting Adjusted Plus-Minus in Soccer with FIFA Ratings
[arXiv] [Player Rankings]
Francesca Matano, Lee F. Richardson, Taylor Pospisil, Collin A. Politsch, and Jining Qin
Submitted to the Journal of Quantitative Analysis in Sports. -
Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe
[Link]
Collin A. Politsch
Carnegie Mellon University, Ph.D. Dissertation, June 2020.
Finalist for best paper in the ASA Astrostatistics Student Paper Competition, sponsored by the Astrostatistics Interest Group.
News
07/2021 – New Preprint submitted to PNAS
A new 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 submitted to the Proceedings of the National Academy of Sciences.
05/2021 – Award for Best Ph.D. Dissertation
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 & Data Science at Carnegie Mellon University. [Link to Dissertation]
04/2021 – New Postdoc Position at Cambridge!
I accepted a postdoc offer to join the Institute of Astronomy and the Department of Pure Mathematics and Mathematical Statistics at the University of Cambridge, where I will resume research at the interface of astrophysics, statistics, and machine learning under the supervision of Kaisey Mandel.
02/2021 – JSM Session on Cosmostatistics
I organized a JSM 2021 session Statistical Challenges in Cosmology, in which some statistician-cosmologists, cosmologist-statisticians, and I will give an overview of this growing nexus field and some of our respective research within it.
01/2021 – New Preprint submitted to JQAS
A co-authored manuscript "Augmenting Adjusted Plus-Minus in Soccer with FIFA Ratings" was submitted to the Journal of Quantitative Analysis in Sports (Co-1st authors: Francesca Matano and Lee F. Richardson) [arXiv] [Player Rankings]
08/2020 – New Postdoc Position in the CMU Machine Learning Department!
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 forecasting various COVID-19 signals in the United States in order to help inform a data-driven national pandemic response.
06/2020 – Ph.D. Defense!
I successfully defended my dissertation "Statistical Astrophysics: From Extrasolar Planets to the Large-scale Structure of the Universe", earning a Joint Ph.D. degree in Statistics and Machine Learning from Carnegie Mellon University. [Document]
03/2020 – New Paper in MNRAS
My paper "Trend Filtering - I. A Modern Statistical Tool for Time-Domain Astronomy and Astronomical Spectroscopy" was published in Monthly Notices of the Royal Astronomical Society. [Link] [arXiv] [GitHub]
03/2020 – New Paper in MNRAS
My paper "Trend Filtering - II. Denoising Astronomical Signals with Varying Degrees of Smoothness" was published in Monthly Notices of the Royal Astronomical Society. [Link] [arXiv] [GitHub]
01/2020 – Paper Award
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]
09/2019 – New Article in Chance Magazine
My article "Mapping the Large-Scale Universe through Intergalactic Silhouettes" was published in CHANCE magazine. [Link]
03/2019 – Name Change
I changed my name to Collin Politsch (formerly Collin Eubanks) in honor of my late mother, Carol Politsch.
10/2018 – Hackathon
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).
06-08/2018 – Internship at Uber Headquarters
I interned as a data scientist at Uber Headquarters in San Francisco during the summer of 2018. During this time, 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 that showed significant improvement over the current ranking algorithm in both offline evaluation and online A/B testing, and was subsequently launched.
11/2017 – Hackathon Press Release
CMU detailed some of our hackathon successes in the following press release: CMU Statistics and Data Science Graduate Students Keep Winning Big.
09/2017 – Hackathon
Three of my classmates and I took 2nd place in the 2017 NBA Hackathon (900+ applications, ~200 selected to compete). [Announcement] [Recap]
09/2017 – Hackathon
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 – Hackathon
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]
05/2017 – Ph.D. Dissertation Proposal
I successfully defended my dissertation proposal "Multi-resolution Regression, Divide and Conquer Risk Estimation, and the Large-scale Universe" and advanced to Ph.D. candidacy. [Proposal]