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.

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.