Dr. Marina E. Johnson

Assistant Professor | Information Management and Business Analytics | Feliciano School of Business
Location: 482
Email: johnsonmari@montclair.edu

Biography

Dr. Johnson is an Assistant Professor in the Feliciano School of Business at Montclair State University. Dr. Johnson completed her Ph.D. at The State University of New York, Binghamton. During her Ph.D., she focused on machine learning, data mining, and metaheuristic optimization.

Before becoming an academic, Dr. Johnson has worked in various companies in the industry including Hugo Boss, and Comcast - NBC Universal. During her industry experience, she extensively used optimization, simulation, and predictive modeling tools.

Dr. Johnson also held a faculty position at the University of Dayton (UD), where she became the program coordinator of the Quality Management Minor. At the UD, Professor Johnson collaborated with other researchers across the campus on several grants.

Dr. Johnson's research interests lie in the area of applications of machine learning and optimization, ranging from marketing analytics to health informatics.

Using active learning and entrepreneurial mindset learning principles, Dr. Johnson continuously explores educational technology tools to increase student learning outcomes.

Dr. Johnson enjoys traveling, hiking, and doing yoga.

Expertise

Machine Learning,
Metaheuristic Optimization,
Statistics,
Data Mining,
Data Science,
Business Analytics

Education

  • Ph D, Industrial and Systems Engineering, 2014, State University of New York at Binghamton, Binghamton, NY
  • MS, Management Science, 2010, Dokuz Eylul University (DEU), Izmir, Turkey
  • BS, Industrial and Systems Engineering, 2007, Gazi University, Ankara, Turkey

Professional Experience

  • Assistant Professor, Montclair State University. (2018 - Current).
  • Principal Data Scientist/Senior Manager, Comcast - NBC Universal. (2017 - 2018).
  • Assistant Professor, The University of Dayton. (2015 - 2017).
  • Graduate Research Assistant at United Services Hospital & Graduate School, State University of New York, Binghamton. (2010 - 2014).
  • Senior Data Analytics Consultant, Hugo Boss. (2007 - 2010).

Honors and Awards

  • Best Paper Award , Information and Communication Tecnologies in Organizations and Society (ICTO) Conference 2019. (October 2019).
  • Associate Member , Alpha Pi Mu Honor Society. (January 2012).

Refereed Published Articles

  • Johnson, M., Jain, R., Brennan-Tonetta, M., Swartz, E., Silver, D., Paolini, J., Mamonov, S., Hill, C. (2021). Impact of Big Data and Artificial Intelligence On Industry: Developing A Workforce Roadmap for a Data Driven Economy. Global Journal of Flexible Systems Management,
  • Johnson, M., Misra, R., Berenson, M. (2021). Integrating Bayesian and Markov Methods in Business Analytics Curricula. Decision Sciences Journal of Innovative Education (DSJIE),
  • Johnson, M., Albizri, A., Harfouche, A. (2021). Responsible Artificial Intelligence in Healthcare: Predicting and Preventing Insurance Claim Denials for Economic and Social Wellbeing. Information Systems Frontiers,
  • Johnson, A., Johnson, M., Nagarur, N. (2021). Supply Chain Design Under Disruptions Considering Risk Mitigation Strategies for Robustness and Resiliency. International Journal of Logistics Systems & Management (IJLSM), 38 (1), pp. pp.1 - 29.
  • Swartz, E., Brennan-Tonetta, P., Jain, R., Johnson, M., Mamonov, S., Hale, M., Jayaraman, J. In search of pedagogical approaches to teaching business ethics in the era of digital transformation. Journal of Big Data - Theory and Practice,
  • Alnsour, Y., Johnson, M., Albizri, A., Harfouche, A. Predicting Patient Length of Stay Using Artificial intelligence to Assist Health Care Professionals in Resource Planning and Scheduling Decisions. Journal of Global Information Management,
  • Jain, R., Johnson, M., Albizri, A., Elias, G. An Open System Architecture Framework for Interoperability (OSAFI). International Journal of Business Information Systems,
  • Johnson, M., Albizri, A., Simsek, S. (2020). Artificial Intelligence in Healthcare Operations to Enhance Treatment Outcomes: A Framework to Predict Lung Cancer Prognosis for Tailoring of Treatment Strategies. Annals of Operations Research,
  • Johnson, M., Albizri, A., Jain, R. (2020). Exploratory Analysis to Identify Concepts, Skills, Knowledge, & Tools to Educate Business Analytics Practitioners. Decision Sciences Journal of Innovative Education, 18 (1), pp. 90-118.
  • Simsek, S., Albizri, A., Johnson, M., Custis, T., Weikert, S. (2020). Predictive Data Analytics for Contract Renewals: A Decision Support Tool for Managerial Decision Making. Journal of Enterprise Information Management,
  • Johnson, M., Berenson, M. (2019). Choosing Among Computational Software Tools to Enhance Learning in Introductory Business Statistics. The Decision Sciences Journal of Innovative Education (DSJIE), 17 (33), pp. 214 - 238.
  • Appiah-Kubi, P., Johnson, M., Trappe, E. (2019). Service Learning in Engineering Technology: Do Students Have Preferences on Project Types?. Journal of Engineering Technology, 36 (1), pp. 32 - 42.
  • Johnson, M., Nagarur, N., (2015). Multi-stage Methodology to Detect Health Insurance Claim Fraud. Healthcare Management Science, 19 (3), pp. 249 - 260.

Published Proceedings

  • Johnson, M., Berenson, M. (2018). Comparing Two Free Software Platforms for Teaching an Introductory Business Statistics Course. : Decision Science Institute Annual Conference. Refereed
  • Johnson, M., Johnson, A., Nagarur, N. (2018). Conceptual Framework for Designing Robust and Resilient Supply Chains. : Decision Sciences Institute Annual Conference. Refereed
  • Albizri, A., Johnson, M., Jain, R. (2018). The Future of Business Analytics Education: Closing the Gap between Academia & Industry. : 2018 DSI Annual Meeting Proceedings. Refereed
  • Alazzam, A., Johnson, M., Lewis, H. (2013). A New Optimization Algorithm for Non-convex Problems. : Proceedings of the 2013 Industrial and Systems Engineering Research Conference. Refereed
  • Johnson, M., Han, Y., Nagarur, N., (2013). A Simulation Model for Blood Supply Chain Systems. : Proceedings of the 2013 Annual Industrial and Systems Engineering Research Conference. Refereed