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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


  1. 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 Nature

  2. 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), p. 4005-4018, 2020, DOI: 10.1093/mnras/staa106

  3. Finalist for best paper in the ASA Astrostatistics Student Paper Competition, sponsored by the Astrostatistics Interest Group. Awarded at JSM in August 2020.


  4. 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

  5. Finalist for best paper in the ASA Astrostatistics Student Paper Competition, sponsored by the Astrostatistics Interest Group. Awarded at JSM in August 2020.


  6. 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

  7. 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)

  8. 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)

  9. Statistical Methods for Estimating Regression Quantiles [Link]
    Collin A. Politsch, Purvasha Chakravarti, and Jining Qin.

  10. 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

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.

Contact


Email: capolitsch [at] cmu [dot] edu