Kathryn L. Colborn, PhD, MSPH

Kathryn Colborn, PhD, MSPH

Education and Training

  • BS, Health and Exercise Science, Colorado State University (2003)
  • MSPH, Biostatistics, Tulane University (2005)
  • PhD, Biostatistics, University of California, Berkeley (2013)
  • Postdoctoral Associate, Institute for Disease Modeling (2014)

Professional Experience

2014 - 2015 Biostatistician Consultant, Centers for Disease Control and Prevention, President's Emergency Plan for AIDS Relief, Maputo, Mozambique
2015 - 2017 Assistant Research Professor, University of Colorado Anschutz Medical Campus, Division of Health Care Policy and Research, Aurora, CO
2017 - 2019 Assistant Professor, Department of Biostatistics and Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO
2016 -Director, Data Informatics and Statistics Core, Palliative Care Research Cooperative Group
2019 -Assistant Professor, Department of Surgery, University of Colorado Anschutz Medical Campus, Aurora, CO
2019 -Research Director, Surgical Outcomes and Applied Research Program, Department of Surgery, University of Colorado Anschutz Medical Campus

Research and Grants



PI Grants:

  • Identification of postoperative infections using electronic health record and administrative claims data (04/01/18-03/31/20). Agency for Healthcare Research and Quality, R03 HS026019, $99,952, PI
  • Development of an automated early warning system for malaria transmission using machine learning (11/01/16-04/29/18). Bill and Melinda Gates Foundation, Grand Challenges Explorations, OPP1161891, $100,000, PI
  • Identification of surgical site infections using machine learning (07/01/16-06/30/18). Data Science to Patient Value, University of Colorado School of Medicine, Pilot, $57,000, multi-PI


  • National Institutes of Health Loan Repayment Program (2018-2020)

Research Interests

  • Automated surveillance of postoperative complications using electronic health record data and machine learning
  • Statistical methodologies for the development and validation of clinical prediction models
  • Surgical outcomes and health services research
  • Methodologies for improving rare event classification
  • Machine learning and high dimensional model selection
  • Design and analysis of cluster-randomized trials