Milton Soto-Ferrari is an Associate Professor at Old Dominion University with a joint appointment in the School of Supply Chain, Logistics & Maritime Operations and the Department of Information Technology & Decision Sciences at the Strome College of Business.
Prior to joining ODU, Dr. Soto-Ferrari spent eight years at Indiana State University, where his contributions to both teaching and scholarship were recognized with the Scott College of Business Award for Teaching (2023), the Scott College of Business Award for Research (2024), and the 2025 Caleb Mills Award for Institutional Excellence in Teaching. At ODU, he serves as an E.V. Williams Faculty Fellow for the Business of Healthcare, a three-year appointment to build cross-college research collaborations connecting business expertise to healthcare challenges.
His research spans AI/ML forecasting, business and healthcare analytics, supply chain management, and business education, with published work in journals such as Computers & Industrial Engineering, Journal of Forecasting, Journal of Statistics and Data Science Education, and the Journal of Information Systems Education, among others.
Articles
- Chams-Anturi, O., Escorcia-Caballero, J. P.. and Soto-Ferrari, M. (2026). Evaluating entrepreneurial intentions of health science students in higher education. International Journal of Innovation Science 18 (1) , pp. 109-129.
- Soto‐Ferrari, M. (2025). Integrating Google Mobility Indices for Forecasting Infectious Diseases Incidence: A Multi‐Country Study on COVID‐19 With LightGBM. Journal of Forecasting.
- Soto-Ferrari, M. (2025). SOT-FER: A multi-tier entropy-based time series forecasting framework with an application to manufacturing. Computers & Industrial Engineering 204 , pp. 111071.
- Soto-Ferrari, M., Carrasco-Pena, A. and Prieto, D. (2025). EpiForecaster: a novel deep learning ensemble optimization approach to combining forecasts for emerging epidemic outbreaks. Stochastic Environmental Research and Risk Assessment 39 (2) , pp. 675-695.
- DePaolo, C. and Soto-Ferrari, M. (2024). Healthcare Analytics Teaching Cases. Journal of Statistics and Data Science Education 33 (3) , pp. 301-312.
- Soto-Ferrari, M., Bhattacharyya, K. and Schikora, P. (2023). POST-BaLSTM: A Bagged LSTM forecasting ensemble embedded with a postponement framework to target the semiconductor shortage in the automotive industry. Computers & Industrial Engineering 185 , pp. 109602.
- Soto-Ferrari, M., Carrasco-Pena, A. and Prieto, D. (2023). AGGFORCLUS: A hybrid methodology integrating forecasting with clustering to assess mitigation plans and contagion risk in pandemic outbreaks: the COVID-19 Case Study. Journal of Business Analytics 6 (3) , pp. 217-242.
- Chams-Anturi, O., Soto-Ferrari, M., Gomez, A., Escorcia-Caballero, J., Romero-Rodriguez, D. and Casile, M. (2022). A Comprehensive Business Process Management Application to Evaluate and Improve the Importations Practices on Big-box Stores. Operations and Supply Chain Management: An International Journal , pp. 164-173.
- Casile, M., Gerard, J. G.. and Soto-Ferrari, M. (2021). Gender differences in self-efficacy, acceptance, and satisfaction in business simulations. The International Journal of Management Education 19 (2) , pp. 100473.
- Prieto, D., Soto-Ferrari, M., Tija, R., Peña, L., Burke, L., Miller, L., Berndt, K., Hill, B., Haghsenas, J., Maltz, E., White, E., Atwood, M. and Norman, E. (2019). Literature review of data-based models for identification of factors associated with racial disparities in breast cancer mortality. Health Systems 8 (2) , pp. 75-98.
- Soto-Ferrari, M., Prieto, D. and Munene, G. (2017). A Bayesian network and heuristic approach for systematic characterization of radiotherapy receipt after breast-conservation surgery. BMC Medical Informatics and Decision Making 17 (1).
Book Chapters
- Soto-Ferrari, M., Chams-Anturi, O., Escorcia-Caballero, J. P.., Hussain, N. and Khan, M. (2019). Evaluation of Bottom-Up and Top-Down Strategies for Aggregated Forecasts: State Space Models and ARIMA Applications Lecture Notes in Computer Science (pp. 413-427) Springer International Publishing.
Conference Proceeding
- Lin, P., Soto-Ferrari, M. and Chams-Anturi, O. (2022). A Logistic Regression Assessment to Measure Radiotherapy Clinical Pathway Concordance for Early Stages Breast Cancer Patients (pp. 559-564) Procedia Computer Science.
- Soto-Ferrari, M., Holvenstot, P., Prieto, D., de Doncker, E. and Kapenga, J. (2013). Parallel Programming Approaches for an Agent-based Simulation of Concurrent Pandemic and Seasonal Influenza Outbreaks (pp. 2187-2192) Procedia Computer Science.