Research Interests
Explainable AI, predictive analytics, simulations, decision science, and healthcare
Education
- Ph D: Operations Management, (2010), Department of Management Information Systems, The University of Arizona - Tucson, AZ
Supporting Area: Economics Minor - MS: Management Information Systems, (2005), Department of Management Information Systems, The University of Arizona - Tucson, AZ
- BEE: Electrical Engineering (Honors), (1998), Sirindhorn International Institute of Technology, Thammasat University - Thailand
Biography
Nichalin Summerfield is an Associate Professor in Operations & Information Systems and the M.S. in Business Analytics program coordinator at UMass Lowell. She holds a Ph.D. in Operations Management and an M.S. in Management Information Systems from the University of Arizona. Her research integrates explainable AI, simulation modeling, game theory, and statistical tools to address operational challenges in healthcare and logistics. Her work has been published in the European Journal of Operational Research, Decision Sciences, Decision Support Systems, the Journal of the American Medical Informatics Association, and the Journal of the Operational Research Society, among others. Before pursuing her Ph.D., she worked at Accenture (Thailand), where she was involved in IT process redesign and system integration projects. While studying at the University of Arizona, she also worked part-time as a software engineer, developing image compression software used on the Phoenix Mars Lander.
Selected Awards and Honors
- Department's Research Excellence Award (2025), Scholarship/Research - Operations & Information Systems Department, UMass Lowell
- Department's Teaching Excellence Award (2022), Teaching - Operations & Information Systems Department, UMass Lowell
- Runner-up - DSI Regional Best Paper Award (2021), Scholarship/Research - Decision Sciences Institute (DSI)
- Best Application of Theory Paper Award (2021), Scholarship/Research - Northeast Decision Sciences Institute (NEDSI)
Selected Publications
- Mahmud, I., Wei, J., Summerfield, N. (2025). Risk Assessment Framework Development for Mobile Banking Systems. Journal of Computer Information Systems, 1-18.
- Nasir, M. (Wichita State University), Summerfield, N., Simsek, S. (Montclair State University), Oztekin, A. (2024). An Interpretable Machine Learning Methodology to Generate Interaction Effect Hypotheses from Complex Datasets. Decision Sciences, 55(6) 549-576.
- Nasir, M. (Wichita State University), Summerfield, N., Carreiro, S. (University of Massachusetts Medical School & UMass Memorial Healthcare), Berlowitz, D., Oztekin, A. (2024). A Machine Learning Approach for Diagnostic and Prognostic Predictions, Key Risk Factors & Interactions. Health Services and Outcomes Research Methodology.
- Ahmed, A., Deokar, A., Lee, H., Summerfield, N. (2024). The role of commitment in online reputation systems: An empirical study of express delivery promise in an e-commerce platform. Decision Support Systems, 176 114061.
- Nasir, M., Summerfield, N., Oztekin, A., Knight, M., Ackerson, L.K., Carreiro, S. (2021). Machine Learning-based Outcome Prediction and Novel Hypotheses Generation for Substance Use Disorder Treatment. Journal of the American Medical Informatics Association.
- Summerfield, N., Deokar, A., Xu, M., Zhu, W. (2021). Should drivers cooperate? Performance evaluation of cooperative navigation on simulated road networks using network DEA. Journal of the Operational Research Society , 72(5) 1042-1057.
- Summerfield, N.S., Dror, M., Cohen, M.A. (2015). City streets parking enforcement inspection decisions: The Chinese postman’s perspective. European Journal of Operational Research, 242(1) 149-160.
- Summerfield, N.S., Dror, M. (2013). Biform game: Reflection as a stochastic programming problem. International Journal of Production Economics, 142(1) 124-129.
- Summerfield, N.S., Dror, M. (2012). Stochastic programming for decentralized newsvendor with transshipment. International Journal of Production Economics, 137(2) 292-303.
- Suakkaphong, N., Zhang, Z., Chen, H. (2011). Disease named entity recognition using semisupervised learning and conditional random fields. Journal of the American Society for Information Science and Technology, 62(4) 727-737.
Selected Presentations
- Machine Learning-based Dropout Prediction in Patients with Substance Use and Mental Health Disorder - 2021 Decision Sciences Institute Annual Conference, November 2021 - Virtual
- Simple Interaction Finding Technique (SIFT) – A Simple Methodology to Generate Novel Hypotheses from Complex Datasets - 2021 Decision Sciences Institute Annual Conference, November 2021 - Virtual
- Simple Interaction Finding Technique (SIFT) – A Simple Methodology to Generate Novel Hypotheses from Complex Datasets - 2021 INFORMS Annual Meeting, October 2021 - Virtual
- A Close Look Into The Substance Epidemic and Substance Use Disorder Problem: A Holistic Data Analytics Approach - 2019 Decision Sciences Institute Annual Conference, November 2019 - New Orleans, LA
- Predicting Patient No-shows via a Hybrid Business Analytics Methodology - 2019 INFORMS Annual Meeting, October 2019 - Seattle, WA
- Predicting Patient No-Shows via a Hybrid Business Analytics Methodology - Decision Sciences Institute Annual Conference, November 2018 - Chicago, IL
- Predicting Patient No-Shows via a Hybrid Business Analytics Methodology - INFORMS 13th Data Mining & Decision Analytics Workshop, November 2018 - Phoenix, AZ
- A Holistic Data Analytic Approach to Determine Impacts of the Caregiver Advise, Record, Enable (CARE) Act on Reducing Readmission and Mortality Rates among Older Adults - INFORMS Annual Meeting, November 2018 - Phoenix, AZ
Selected Contracts, Fellowships, Grants and Sponsored Research
- Poll Worker Assignment and Voting Machine Allocation across Electoral Precincts (2024), Grant - Internal Seed Funding Program
Fontem, B. (Principal), Summerfield, N. (Co-Principal) - Leverage Data Analytics and Game Theory to Study Complex Urban Transportation (2017), Grant - Internal Seed Funding Program
Summerfield, N. (Co-Principal), Chen, D. (Principal), Deokar, A. (Co-Principal)
Research Currently in Progress
- Deep Reinforcement Learning Application in Supply Chain Optimization
Summerfield, N., Song, J., Sharma, P.