Institute of Astronomy

Dr Collin Politsch

E-mail: collin.politsch@ast.cam.ac.uk

Personal Homepage

Office: Kavli K08
Office Tel: 46430
More Info (Internal)

Preprints
Publications

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

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.

P. D. Aleo, K. Malanchev, S. Sharief, D. O. Jones, et al. The Young Supernova Experiment Data Release 1 (YSE DR1): Light Curves and Photometric Classification of 1975 Supernovae. Submitted to ApJ.
[arXiv]

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 Institute of Astronomy, the Kavli Institute for Cosmology Cambridge, and the Statistical Laboratory of the Department of Pure Mathematics and Mathematical Statistics. His research focuses on problems at the interface of astrophysics, statistics, and machine learning. 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]

Professional Service

  • Program Chair-Elect: American Statistical Association Astrostatistics Interest Group
  • Referee: A&C, JCAP, NASA EPSCoR, CHANCE

Professional Memberships

  • AAS American Astronomical Society
  • ASA American Statistical Association
  • COIN Cosmostatistics Initiative
  • IAA International Astrostatistics Association
  • IAIA International AstroInformatics Association           
Page last updated: 13 December 2022 at 00:10