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.” experience. He has been investigating how data analytical methods, probabilistic graphical models, operations research, statistics, and simulation can be effectively integrated into real-world cases for managers and practitioners to make interpretable, reliable, data-informed, and cost-effective decisions. The main theme of his work has been to design probabilistic graphical models; Bayesian Belief Networks (BBN) and Markov Networks (MN), and integrated models with data mining techniques to better understand and extract information from big data such that new and useful knowledge is generated for data-driven analytical decisions.

Peer-Reviewed Publications

1. Topuz, K. and Delen, D. A probabilistic Bayesian inference model to investigate injury severity in automobile crashes. Decision Support Systems, page 113557, 2021. Publisher: Elsevier

2. Topuz, K., Jones, B. D., Sahbaz, S., and Moqbel, M. Methodology to combine theoretical knowledge with a data-driven probabilistic graphical model. Journal of Business Analytics, pages 1–15, 2021. Publisher: Taylor & Francis

3. Eryarsoy, E., Delen, D., Davazdahemami, B., and Topuz, K. A novel diffusion-based model for estimating cases, and fatalities in epidemics: The case of COVID-19. Journal of Business Research, 124:163–178, 2021. Publisher: Elsevier

4. Moqbel, M., Bartelt, V. L., Topuz, K., and Gehrt, K. L. Enterprise social media: combating turnover in businesses. Internet Research, 2020. Publisher: Emerald Publishing Limited

5. Zengul, F. D., Lee, T., Delen, D., Almehmi, A., Ivankova, N. V., Mehta, T., and Topuz, K. Research Themes and Trends in Ten Top-Ranked Nephrology Journals: A Text Mining Analysis. American Journal of Nephrology, 51(2):147–159, 2020. Publisher: Karger Publishers

6. Delen, D., Topuz, K., and Eryarsoy, E. Development of a Bayesian Belief Network-based DSS for predicting and understanding freshmen student attrition. European Journal of Operational Research, 281(3):575–587, 2020. Publisher: Elsevier

7. Rouyendegh, B. D., Topuz, K., Dag, A., and Oztekin, A. An AHP-IFT integrated model for performance evaluation of E-commerce web sites. Information Systems Frontiers, 21(6):1345– 1355, 2019. Publisher: Springer

8. Topuz, K., Uner, H., Oztekin, A., and Yildirim, M. B. 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, 2018. Publisher: Springer

9. Topuz, K., Zengul, F. D., Dag, A., Almehmi, A., and Yildirim, M. B. Predicting graft survival among kidney transplant recipients: A Bayesian decision support model. Decision Support Systems, 106:97–109, 2018. Publisher: Elsevier

10. Topuz, K., Shepherd, S., Paiva, W. D., Delen, D., Schumacher, K., Sathyanarayanan, S. R., and Yildirim, M. B. A probabilistic scoring model to evaluate providers regarding continuity of care from a relational perspective. In AMIA, 2017

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

12. Dag, A., Topuz, K., Oztekin, A., Bulur, S., and Megahed, F. M. A probabilistic data-driven framework for scoring the preoperative recipient-donor heart transplant survival. Decision Support Systems, 86:1–12, 2016. Publisher: Elsevier

13. Luxhøj, J. T. and Topuz, K. Probabilistic Causal Modeling of Runway Incursion (RI) Safety Risk. In IIE Annual Conference and Expo 2013, 2013

14. Luxhoj, J. T. and Topuz, K. Sensitivity Analyses of Risk Factors and Mitigation Effects on a Wake Vortex Encounter Flight Scenario. In Industrial and Systems Engineering Research Conference, 2012