Bio

Maria Mayorga is the director and Goodnight Distinguished Chair in Operations Research. She joined North Carolina State University in August 2013 as a Chancellor’s Faculty Excellence Program cluster hire in personalized medicine. She is a professor in the Edward P. Fitts Department of Industrial and Systems Engineering, part of the Healthcare Systems Engineering group. Her goal is to address fundamental research barriers in moving from estimates of efficacy to estimates of the effectiveness of interventions or policies by explicitly considering individual patient preferences when the underlying patient population is heterogeneous. She is also interested in optimally allocating resources in emergency medical service systems. To achieve these goals, Mayorga will create analytical models of health systems that incorporate patient-level data. She uses techniques such as simulation, dynamic programming, applied probability, queuing theory and mathematical programming. She employs multiple sources of secondary data and a mixed methods approach to enable predictions of health outcomes at levels for which it is difficult to conduct studies in practice. This research is inherently interdisciplinary and is thus facilitated via collaborations with health services researchers such as epidemiologists, economists, and medical doctors.

Before joining the NC State faculty, she was on the faculty at Clemson University, Department of Industrial Engineering for seven years. She has authored over 90 publications in archival journals and refereed proceedings. Her research has been supported by NIH and NSF, among others. She received the distinguished National Science Foundation CAREER Award for her work to incorporate patient choice into predictive models of health outcomes.

Area(s) of Expertise

Her research interests include predictive models in health care, healthcare operations management, emergency response, and humanitarian logistics. Her goal is to use operations research to make recommendations that have a broad impact, inform policy-level decisions and reduce health disparities. She employs a variety of methods, including mathematical models, statistics, simulation, and, more recently, machine learning and artificial intelligence.

Publications

• Data from: Social vulnerability, capacity, and economic outcomes in North Carolina populations , DRYAD (2026)
• Reducing Manual Labeling Effort in Imbalanced Data Sets: Active Learning for Detecting Illicit Massage Business Reviews , Operations Research (2026)
• Advancing Simulation in Healthcare and Life Sciences: A Panel Discussion of Future Research Directions , (2025)
• Decision Tree Framework for Selecting Evidence-Based Colorectal Cancer Screening Interventions Using Metamodels , (2025)
• Estimating the long-term health impact and program cost-effectiveness of providing direct screening services to low-income, medically underserved patients through the Colorectal Cancer Control Program (CRCCP) , Cancer Causes & Control (2025)
• Modeling Social Influence on Covid-19 Vaccination Uptake Within an Agent-Based Model , (2025)
• Optimizing Masks and Random Screening Test Usage within K-12 Schools , MDM Policy & Practice (2025)
• Predicting patient enrollment in a telephone-based principal care management service using topic modeling , PLOS Digital Health (2025)
• COVSIM: A stochastic agent-based COVID-19 SIMulation model for North Carolina , Epidemics (2024)
• Incorporating Face Mask Usage in Agent-Based Models Using Personal Beliefs and Perceptions: An Application of the Health Belief Model , 2024 WINTER SIMULATION CONFERENCE, WSC (2024)