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Eyyub Kibis

Assistant Professor, Information Management and Business Analytics

School of Business 489
BS, Bogazici University
MA, University of Houston
MS, University of Houston
PhD, University of Houston
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Dr. Eyyub Kibis is an Assistant Professor at the Feliciano School of Business in the Information Management and Business Analytics (IMBA) department. Prior to teaching at Montclair State, he was an Assistant Professor of Business Analytics at the College of Saint Rose in Albany, NY. He is a member of several professional organizations including the Industrial Engineering Honor Society, Alpha Phi Mu, INFORMS, Production and Operations Management (POM), and Decision Sciences Institute (DSI). He is currently the co-chair of the INFORMS Workshop on Data Mining and Decision Analysis.


  • A deterministic makeshift hospital utilization model for Covid -19 treatment: A model for developing countries
  • A holistic approach for prediction of student persistence and retention
  • A Hybrid Tree-Augmented Bayesian Belief Approach to Predict Misstatements in Financial Reporting
  • A tree augmented Bayesian belief approach to predict IPO valuations and the money left on the table
  • An integrated machine learning approach with mixed integer linear programming: Determining optimal chemotherapy dosage for stage II breast cancer patients
  • Optimizing Long-Term Pilot Planning in the Face of Disruptions: A Model for Resilience in Single Fleet Airline
  • Stochastic dynamic resource allocation for Covid-19 prevention and treatment: An approximate dynamic programming approach

Professional Experience

  • Assistant Professor of Business Analytics, The College of Saint Rose (2017 - 2020)

Honors & Awards

  • D. W. Hodgson Outstanding Doctoral-level Student Award, Wichita State University (November 2015)
  • Donald D. Sbarra Endowed Fellowship, Wichita State University (November 2014)
  • Ollie A. & J.O. Heskett Graduate Fellowship, Wichita State University (April 2015)
  • 2019 INFORMS MIF Best Paper Finalist, 2019, INFORMS (November 2019)
  • Best Graduate Research Award, Wichita State University (April 2016)
  • The Best Paper Award, 2022 Annual Informs Conference - ENRE Society (October 2022)
  • Poster Presentation Award - Second Place, 2022 INFORMS Business Analytics Conference (April 2022)
  • Harvey J. Greenberg Research Award - Honorable Mention, 2022 Annual Informs Conference - Computing Society (October 2022)

Refereed Published Articles

  • E. Kibis, I. Buyuktahtakin (2017). Optimizing invasive species management: A mixed-integer linear programming approach. European Journal of Operational Research
  • I. Buyuktahtakin, E. Kibis, H. Cobuloglu, G. Houseman, T. Lampe (2015). An age- structured bio-economic model of invasive species management: insights and strategies for optimal control. Biological Invasions
  • E. Kibis, I. Buyuktahtakin, R. Haight, N. Akhundov, K. Knight, C. Flower (2020). A new multi-stage stochastic programming model and cutting planes for the optimal surveillance and control of emerald ash borer in cities. INFORMS Journal on Computing
  • S. Simsek, U. Kursuncu, E. Kibis, M. AnisAbdellatif, A. Dag (2020). A hybrid data mining approach for identifying the temporal effects of variables associated with breast cancer survival. Expert Systems with Applications
  • E. Kibis, I. Buyuktahtakin (2019). Optimizing multi-modal cancer treatment under 3D spatio-temporal tumor growth. Mathematical Biosciences
  • S. Simsek, U. Kursuncu, E. Kibis, A. Dag (2018). A Machine Learning-Based Holistic Approach to Predict the Survival of Breast Cancer Patients. International Journal of Electrical, Electronics and Data Communication
  • I. Buyuktahtakm, E. des-Bordes, E. Kibis (2018). A new epidemics-logistics model: Insights into controlling the Ebola virus disease in West Africa. European Journal of Operational Research
  • A. Dag, Z. Akcam, E. Kibis, S. Simsek, D. Delen (2022). A probabilistic data analytics methodology based on Bayesian Belief network for predicting and understanding breast cancer survival. Knowledge-Based Systems
  • H. Dolatsara, M. Caglar, E. Kibis, S. Simsek, A. Dag, G. Dolatsara, D. Delen (2022). An Interpretable Decision-Support Systems for Daily Cryptocurrency Trading. Expert Systems with Applications

Published Proceedings

  • E. Kibis, E. Buyuktahtakin (5s). 2017. Data analytics approaches for breast cancer survivability: Comparison of data mining methods IIE Annual Conference