Dr. Eyyub Y. Kibis

Assistant Professor | Information Management and Business Analytics | Feliciano School of Business
Location: SBUS 489
Email: kibise@montclair.edu

Biography

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.

Education

  • Ph D, Industrial Engineering, 2017, Wichita State University, Wichita, KS
  • MS, Finance, 2013, University of Houston Clear Lake, Houston, TX
  • MA, Economics, 2011, University of Houston, Houston, TX
  • BS, 2009, Bogazici University, Istanbul, Turkey

Professional Experience

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

Honors and Awards

  • 2019 INFORMS MIF Best Paper Finalist, 2019 , INFORMS. (November 2019).
  • Best Graduate Research Award , Wichita State University. (April 2016).
  • D. W. Hodgson Outstanding Doctoral-level Student Award , Wichita State University. (November 2015).
  • Ollie A. & J.O. Heskett Graduate Fellowship , Wichita State University. (April 2015).
  • Donald D. Sbarra Endowed Fellowship , Wichita State University. (November 2014).

Research

  • A holistic approach for prediction of student persistence and retention
  • A probabilistic data analytics methodology based on Bayesian belief network for predicting and understanding breast cancer survival
  • An integrated machine learning approach with mixed integer linear programming: Determining optimal chemotherapy dosage for stage II breast cancer patients
  • An interpretable approach for predicting bitcoin price volatility
  • Do analysts mislead practitioners? A comprehensive analytics technique to better detect non-surviving cancer patients

Refereed Published Articles

  • Kibis, E., Buyuktahtakin, I., Haight, R., Akhundov, N., Knight, K., Flower, C. (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,
  • Simsek, S., Kursuncu, U., Kibis, E., AnisAbdellatif, M., Dag, A. (2020). A hybrid data mining approach for identifying the temporal effects of variables associated with breast cancer survival. Expert Systems with Applications, 139
  • Kibis, E., Buyuktahtakin, I. (2019). Optimizing multi-modal cancer treatment under 3D spatio-temporal tumor growth. Mathematical Biosciences, 307 pp. 53-69.
  • Simsek, S., Kursuncu, U., Kibis, E., Dag, A. (2018). A Machine Learning-Based Holistic Approach to Predict the Survival of Breast Cancer Patients. International Journal of Electrical, Electronics and Data Communication,
  • Buyuktahtakm, I., des-Bordes, E., Kibis, E. (2018). A new epidemics-logistics model: Insights into controlling the Ebola virus disease in West Africa. European Journal of Operational Research, 265 (3), pp. 1046-1063.
  • Kibis, E., Buyuktahtakin, I. (2017). Optimizing invasive species management: A mixed-integer linear programming approach. European Journal of Operational Research, 259 (1), pp. 308-321.
  • Buyuktahtakin, I., Kibis, E., Cobuloglu, H., Houseman, G., Lampe, T. (2015). An age- structured bio-economic model of invasive species management: insights and strategies for optimal control. Biological Invasions, 17 (9), pp. 2545-2563.

Published Proceedings

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