Personal profile
University profile
Dr. Marina 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.
Research interests
Applications of machine learning and optimization, ranging from marketing analytics to health informatics
Scholarly Interests
Machine Learning,
Metaheuristic Optimization,
Statistics,
Data Mining,
Data Science,
Business Analytics
Faculty/Media Expert
Expert on machine learning, artificial intelligence, big data analytics and the healthcare industry, statistical data mining and computational optimization.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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SDG 3 Good Health and Well-being
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SDG 8 Decent Work and Economic Growth
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Collaborations and top research areas from the last five years
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An analytical assessment of sentiment analysis trends and methods through systematic review and topic modeling
Hill, C., Irshaidat, F., Johnson, M. & Fresneda, J., Dec 2025, In: Decision Analytics Journal. 17, 100644.Research output: Contribution to journal › Review article › peer-review
Open Access -
A multi-appointment patient scheduling system with machine learning and optimization
Han, Y., Johnson, M. E., Shan, X. & Khasawneh, M., Mar 2024, In: Decision Analytics Journal. 10, 100392.Research output: Contribution to journal › Article › peer-review
Open Access15 Link opens in a new tab Scopus citations -
Comparing programming languages for data analytics: Accuracy of estimation in Python and R
Hill, C., Du, L., Johnson, M. & McCullough, B. D., 1 May 2024, In: Wiley Interdisciplinary Reviews: Data Mining and Knowledge Discovery. 14, 3, e1531.Research output: Contribution to journal › Article › peer-review
14 Link opens in a new tab Scopus citations -
Exploring the relationship between YouTube video optimisation practices and video rankings for online marketing: a machine learning approach
Johnson, M. E. & Malaga, R. A., 2024, In: Journal of Business Analytics. 7, 2, p. 120-135 16 p.Research output: Contribution to journal › Article › peer-review
4 Link opens in a new tab Scopus citations -
A machine learning decision support system for determining the primary factors impacting cancer survival and their temporal effect
Dag, A. Z., Johnson, M., Kibis, E., Simsek, S., Cankaya, B. & Delen, D., Dec 2023, In: Healthcare Analytics. 4, 100263.Research output: Contribution to journal › Article › peer-review
Open Access7 Link opens in a new tab Scopus citations