Research

Current Interests:

Machine Learning/Statistics: Massive spatial datasets, spatial modeling, distributed spatial models, time series analysis, signal processing, forecasting, data mining, nonparametric statistics, uncertainty quantification, high-dimensional statistics, statistical machine learning

Astrophysics: Astrostatistics and astroinformatics, cosmostatistics, nonparametric and data-driven astrophysics, Lyman-α forest, intergalactic medium, statistical cosmography, large-scale structure of the Universe, planetary transits, supernova cosmology

Selected papers

[Google Scholar]

Collin A. Politsch, Jessi Cisewski-Kehe, Rupert A.C. Croft, and Larry Wasserman. Three-dimensional cosmography of the high redshift Universe using intergalactic absorption. Pre-submission Inquiry approved by Nature.
[Webinar Talk (YouTube)]

Collin A. Politsch, Jessi Cisewski-Kehe, Rupert A.C. Croft, and Larry Wasserman. Trend filtering – I. A Modern Statistical Tool for Astronomical Spectroscopy and Time-Domain Astronomy. Monthly Notices of the Royal Astronomical Society, 492(3), March 2020, p. 4005-4018.
[Publisher] [arXiv] [trendfiltering R package]
Finalist, 2020 Astrostatistics Student Paper Competition.

Collin A. Politsch, Jessi Cisewski-Kehe, Rupert A.C. Croft, and Larry Wasserman. Trend filtering – II. Denoising Astronomical Signals with Varying Degrees of Smoothness. Monthly Notices of the Royal Astronomical Society, 492(3), March 2020, p. 4019-4032.
[Publisher] [arXiv] [trendfiltering R package]
Finalist, 2020 Astrostatistics Student Paper Competition.

E. Y. Cramer, E. L. Ray, V. K. Lopez, et al. Evaluation of individual and ensemble probabilistic forecasts of COVID-19 mortality in the US. Proceedings of the National Academy of Sciences, Volume 119, Issue 15, April 2022.
[Publisher] [medRxiv] [COVID-19 Forecast Hub]

Career

Dr. Politsch is a postdoctoral researcher in the Kavli Institute for Cosmology Cambridge and the Institute of Astronomy. His research focuses on problems at the interface of astrophysics, statistics, machine learning, and computing. He is currently the Program Chair-Elect of the Astrostatistics Interest Group of the American Statistical Association.

Prior to Cambridge, Dr. Politsch was a postdoctoral fellow in the Machine Learning Department at Carnegie Mellon University, where he was a core member of the CMU-based Delphi Group and Team Lead of the forecasting development and evaluation team. Under the supervision of Prof. Ryan Tibshirani (Delphi PI), Dr. Politsch and his team devoted their 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. Dr. Politsch also held a guest researcher appointment at the Flatiron Institute's Center for Computational Astrophysics in New York.

Dr. Politsch 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. His 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 his Ph.D., Dr. Politsch received an M.Sc. in Machine Learning from Carnegie Mellon and a B.Sc. in Mathematics from the University of Kansas.

Qualifications

• Joint Ph.D. in Statistics and Machine Learning
  Carnegie Mellon University, 2020

• M.Sc. in Machine Learning
  Carnegie Mellon University, 2017

• B.Sc. in Mathematics (With Honors)
  University of Kansas, 2014

Awards and Prizes

• 2020-’21 Umesh K. Gavaskar Memorial Award for Best Ph.D. Dissertation in Statistics and Data Science at Carnegie Mellon University. [Dissertation]

• Finalist for best paper in the 2020 ASA Astrostatistics Student Paper Competition, sponsored by the Astrostatistics Interest Group. [Link]

• 2021 Statistical Partnerships Among Academe, Industry, and Government (SPAIG) Award [Link]
  • Awarded to the Delphi group (in conjunction with our government and corporate partners: the U.S. Centers for Disease Control and Prevention, Google, Facebook, Amazon, Change Healthcare, Optum, and Quidel Inc.) by the American Statistical Association “for commitment to the theory and practice of epidemic tracking and forecasting through building and modeling unique public health data streams,” for our partnership on COVIDcast.

• 2021 Allen Newell Award for Research Excellence [Link]
  • Awarded to the Delphi Group by the Carnegie Mellon University School of Computer Science, “for advancing the theory and practice of epidemic tracking and forecasting, and enabling national collaborative scientific response.”

 

Departmental Roles

• Postdoc representative on the department EDI Committee
• Co-Chair of the department EDI Work-Life Balance focus group
• Postdoc representative on the IOP Project Juno Self-Assessment Team
• Postdoc representative on the department Teaching Committee
• Member of the department Postdoc Committee

Professional Service

• Program Chair-Elect: American Statistical Association Astrostatistics Interest Group

• Referee: A&C, JCAP, NASA EPSCoR, CHANCE

Scientific Collaborations

• Delphi Delphi Group [Link] (2020 – 2021)
• LSST Large Synoptic Survey Telescope (Vera C. Rubin Observatory)
• WEAVE William Herschel Telescope Enhanced Area Velocity Explorer
• YSE Young Supernova Experiment

Professional Memberships

• AAS American Astronomical Society
• ASA American Statistical Association
• COIN Cosmostatistics Initiative
• IAA International Astrostatistics Association
• IAIA International AstroInformatics Association