Kazim Topuz, PhD

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


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


Dr. Kazim Topuz is an Assistant Professor of Business Analytics & Operations Management in the Collins College of Business at Tulsa University (TU). Prior to joining TU, he taught 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. He is a member of the Industrial Engineering Honor Society, Alpha Phi Mu, INFORMS, Decision Sciences Institute (DSI), and American Medical Informatics Association (AMIA). He has served as a session chair and organized workshops at the national and international conferences. His work has been published in high-impact journals such as Decision Support Systems, European Journal of Operational Research, Information Systems Frontiers, Journal of Transportation and Health, and Annals of Operations Research, among others. He is also serving as an Editor of the Journal of Business Analytics, Business Analytics & Big Data section for the Journal of Modelling in Management, and Information Systems Frontiers journal. A big part of his life and his research can be summarized with this latter phrase and W. Edwards Deming’s famous quote “In God we trust; all others bring data.” He has been investigating how data analytical methods, probabilistic graphical models, operations research, statistics, and simulation can be effectively integrated into healthcare, retention, transportation, and aviation for managers and practitioners to make reliable, data-informed and cost-effective decisions. As an instructor, he has a passion for guiding students through the learning process as well as a passion for the material he presents. He believes one of the best ways to foster learning is to demonstrate your passion for the subject you are teaching to students. He encourages learning by creating a relaxed environment for students, stimulating conversation about concepts being presented, and organizing material in a way that makes it easier to understand. Dr. Topuz is the recipient of the “Outstanding Doctoral-level Student” award, and he was selected to be a member of Alpha Pi Mu honor society, "recognition as someone who has shown exceptional academic interests, abilities, and leadership in the field of industrial engineering." In addition, he achieved first place in a National Case Study Completion and ranked top 0.1% among 0.5 million university graduates in Academic Graduate Exam (GRE) in Turkey.

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


Journal Articles

  • TopuzK. (2019). Probabilistic Model for Predicting the Risk Level of Graft- Donor Matching for Liver Transplantation. In . Decision Science Institute Annual Conference.
  • Delen, D., Topuz, K., & Eryarsoy, E. (2019). Development of a Bayesian Belief Network-based DSS for predicting and understanding freshmen student attrition. European Journal of Operational Research.

  • 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.

  • TopuzK. A Unifying Framework to Assess Continuity of Care.
  • MoqbalM., & TopuzK. Enterprise Social Media: Combating Techno-stress and Turnover.
  • TopuzK. Probabilistic Decision Support Model for Predicting Freshmen Student Attrition.
  • TopuzK. Primary care clinic appointment scheduling with predicted appointment dependent no-shows and a case study of simulation in pediatrics.
  • ZengulF., TopuzK., IvankovaN., & LeeT. Examination of Research Themes and Trends in Ten Top-ranked Nephrology Journals: A Text Mining Analysis.

Conference Proceedings

  • TopuzK. (2019). Probabilistic Model for Predicting the Risk Level of Graft- Donor Matching for Liver Transplantation. In . Decision Science Institute Annual Conference.
  • TopuzK. (2019). Investigating Aviation Accidents with Machine Learning. In . Decision Science Institute Annual Conference.
  • TopuzK. (2019). Stochastic Optimization Model With Bayesian Network Update: The Case of Breast Cancer Chemotherapy Treatment. In . INFORMS 2019 Annual Meeting.
  • 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
  • American Medical Informatics Association