Kazim Topuz, PhD

Kazim Topuz, PhD
Assistant Professor of Operations Management & Business Analytics
College of Business
Finance, Operations Management and International Business
918-631-2933 Helmerich Hall Room 118-A

Education

PhD – Wichita State University MS – Rutgers University MS – Lehigh University BS – Yildiz Technical Univeristy

Bio

Dr. Kazim Topuz is an Assistant Professor of Operations Management and Business Analytics in the School of Finance, Operations Management and International Business in the Collins College of Business at the Tulsa University (TU). Prior to joining Collins College of Business, he taught at the Division of Management Information Systems in the Price College of Business at the University of Oklahoma (OU) as a lecturer, and he worked for the Center for Health Systems Innovation at Oklahoma State University (OSU) as a Research Fellow. In addition to his doctorate in philosophy (Ph.D.) from the Wichita State University (WSU), Dr. Topuz holds masters` degrees in Information Systems Engineering from Lehigh University, and Industrial and Systems Engineering from Rutgers University.

Research Interests

Business/Data Analytics, Machine Learning, Healthcare Analytics, Predictive and Prescriptive Analytics, Bayesian Belief Networks, Decision Support Systems, Probabilistic Graphical Models

Teaching Interests

Business Analytics, Data Systems, Business Data Analysis, Data Science and Analytics, Operations Management, R and Python Programming for Data Science, Database Management

Publications

Journal Articles

  • Topuz, K., Uner, H., Oztekin, A., & Yildirim, M. (2018). Predicting pediatric clinic no-shows: a decision analytic framework using elastic net and Bayesian belief network. Annals of Operations Research, 263(1-2), 479–499.

  • Rouyendegh, B., Topuz, K., Dag, A., & Oztekin, A. (2018). An AHP-IFT Integrated Model for Performance Evaluation of E-Commerce Web Sites. Information Systems Frontiers, 1–11.

  • Topuz, K., Zengul, F., Dag, A., Almehmi, A., & Yildirim, M. (2018). Predicting graft survival among kidney transplant recipients: A Bayesian decision support model. Decision Support Systems, 106, 97–109.

  • Delen, D., Tomak, L., Topuz, K., & Eryarsoy, E. (2017). Investigating injury severity risk factors in automobile crashes with predictive analytics and sensitivity analysis methods. Journal of Transport and Health, 4, 118–131

  • Dag, A., Topuz, K., Oztekin, A., Bulur, S., & Megahed, F. (2016). A probabilistic data-driven framework for scoring the preoperative recipient-donor heart transplant survival. Decision Support Systems, 86, 1–12.

  • Topuz, K. Probabilistic Model for Predicting the Risk Level of Graft- Donor Matching for Liver Transplantation.
  • Topuz, K. A Unifying Framework to Assess Continuity of Care.
  • Maqbal, M., & Topuz, K. Enterprise Social Media: Combating Techno-stress and Turnover.
  • Delen, D., & Topuz, K. Probabilistic Decision Support Model for Predicting Freshmen Student Attrition.

Conference Proceedings

  • Luxhøj, J., & Topuz, K. (2013). Probabilistic causal modeling of runway incursion (RI) safety risk. In IIE Annual Conference and Expo 2013 (pp. 4086–4095).

Courses Taught

  • Operations Management

Professional Affiliations

  • Decision Science Institute
  • INFORMS
  • American Medical Informatics Association