- Ph.D. in Industrial Engineering from Georgia Institute of Technology
- M.S. in Operations Research from Georgia Institute of Technology
- B.S. in Environmental Engineering Science from Massachusetts Institute of Technology/li>
Dr. Regnier's primary teaching responsibility is executive education for mid-level and senior public-sector managers from the U.S. and around the world. Her teaching has taken her to Central and South America, Europe, Africa, and Asia. Her teaching interests include probability and statistics, risk analysis and management, multiple-criteria decision-making, psychology of decision-making, and logistics management. Dr. Regnier was honored with the Defense Resources Management Institute 2014 Teaching Award.
Dr. Regnier's research brings decision-relevant information about energy, technology, and the natural environment into practical multi-stage decision contexts. Much of predictive analytics responds to the question: “What do we know?” Dr. Regnier's research seeks to answer the follow-on question: “And when will we know it?”. Multistage decisions under uncertainty can be highly dimensional. Using analytics tools including machine learning and optimization algorithms, with decision-analytic approaches to formulation, she develops uncertainty models adapted to the decision context and available data. Dr. Regnier’s work has been funded by the National Science Foundation, Office of Naval Research, the Joint Typhoon Warning Center, the Marine Forces Reserve, and other Department of Defense organizations.
For Marines stationed in New Orleans, Dr. Regnier and Dr. Cameron MacKenzie
built a simulator to give key personnel the opportunity to experience many synthetic storms to gain a familiarity with the evolution of Gulf hurricanes, forecast quality as a function of lead time, and their critical decision points. The simulator is still in development but has already been used in staff training exercises.
Probability Forecast Time Series
Probability forecasts are generated and consumed in many contexts. Examples include the National Hurricane Center’s wind-speed probability forecasts, statistical elections forecasts, and prediction market forecasts for geopolitical and other events. Often, these generate a time series of probability forecasts for each event. Dr. Regnier’s recent work on the properties required of efficient forecasting systems also provides statistical tests to diagnose inefficiency. Her ongoing work focuses on methods for improving these forecasts, for example, the 2014 US Senate election forecasts.
Value-of-Information for late-stage R&D projects
In support the Office of Naval Research’s Energy Systems Technology and Evaluation Program
(ESTEP), Dr. Regnier is working with Dr. Robert Barron
on value-of-information estimates for energy technology demonstration projects
Fully Burdened Cost of Fuel
In support of the Secretary of the Navy’s Energy goals,
Dr. Regnier identified the multiplier effect which affects the Fully Burdened Cost of Fuel (FBCF) in multistage fuel supply networks. Dr. Regnier and Dr. Jay Simon and Dr. Aruna Apte have shown that this effect is especially important in austere environments common in humanitarian assistance and disaster response, and in areas where an adversary is denying access.
See Curriculum Vitae for complete list
Regnier, E.D. & MacKenzie, C.A. (2019) The Hurricane Decision Simulator: A tool for Marine Forces in New Orleans to practice operations management in advance of a hurricane. Manufacturing & Service Operations Management . 21(1): 103-120.
Regnier, E.D. (2018) Probability forecasts made at multiple lead times. Management Science . 64(5) 2407–2426.
Simon, J., Regnier, E. & Whitney, L.K. (2014). A value-focused approach to energy transformation in the United States Department of Defense. Decision Analysis 11(2):117-132.
Slootmaker, L.A., Regnier, E., Hansen, J.A., & Lucas, T.W. (2013). User focus and simulation improve predictions of piracy risk. Interfaces 43(3):256-267.
Regnier, E. & Shechter, S.M. (2013). State-space size considerations for disease-progression models. Statistics in Medicine 32(22):3862-3880.
Regnier, E. (2008). Public evacuation decisions and hurricane track uncertainty. Management Science 54(1): 16-28.
Regnier, E. (2007). Oil and energy price volatility. Energy Economics 29(3): 405-427.
Regnier, E. & Harr, P.A. (2006). A dynamic decision model applied to hurricane landfall. Weather and Forecasting 21(5): 764-780.
Regnier, E., Sharp, G, & Tovey, A. (2004). Replacement under ongoing technological progress. IIE Transactions 36(6): 497-508.