Directory

Ozge Surer

Assistant Professor

Information Systems & Analytics


Profile

Academic Background

  • Ph.D. Northwestern University, Industrial Engineering and Management Sciences, 2020
  • M.S. Bogazici University, Industrial Engineering, 2014
  • B.S. Istanbul Technical University, Industrial Engineering, 2011

Academic & Professional Experience

  • Assistant Professor, Miami University, 2022-Present
  • Postdoctoral Research Fellow, Northwestern University, 2021-2022

Recent Publications

  • Ozge Surer, Filomena M. Nunes, Matthew Plumlee, Stefan M. Wild. Uncertainty quantification in breakup reactions. Physical Review C, 106, 024607, 2022.
  • Ozge Surer, Daniel W. Apley, Edward C. Malthouse. Coefficient tree regression: fast, accurate and interpretable predictive modeling. Machine Learning, 1--38, 2021.
  • Ozge Surer, Daniel W. Apley, Edward C. Malthouse. Coefficient tree regression for generalized linear models. Statistical Analysis and Data Mining: The ASA Data Science Journal, 14, 407--429, 2021.
  • Haoxiang Yang, Ozge Surer, Daniel Duque, David P. Morton, Bismark Singh, Spencer Fox, Remy Pasco, Kelly Pierce, Paul Rathouz, Zhanwei Du, Michael Pignone, Mark E. Escott, Stephen I. Adler, S. Clairborne Johnston, Lauren Ancel Meyers. Design of COVID-19 staged alert systems to ensure healthcare capacity with minimal closures. Nature Communications, 12, 3767, 2021.

Honors & Awards

Biography

Ozge Surer is an Assistant Professor in the Department of Information Systems and Analytics at Miami University. Prior to this appointment, she was a postdoctoral research fellow at the Northwestern-Argonne Institute of Science and Engineering. She obtained her Ph.D. degree in the Industrial Engineering and Management Sciences Department at Northwestern University. She has a bachelor's degree from Istanbul Technical University and a master of science degree from Bogazici University in the same field. Her primary research interests lie at the intersection of statistics and machine learning to analyze and develop tools for learning from large data sets. Her doctoral work focuses on designing interpretable predictive models to discover the group structure from data, and their interdisciplinary applications. Currently, she is developing tools and techniques relying on Bayesian statistical learning to predict the future behavior of physical systems with well-quantified uncertainties.

Ozge Surer's personal website is accessible from the link https://ozgesurer.github.io.

Courses

  • ISA 291
  • A 2:50-4:10 MW FSB0013
  • B 4:25-5:45 MW FSB 0013
Ozge Surer

Contact Information

Office Hours

  • MW 1230-2

Links

* Accessible version of PDF available upon request.