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Risk and Decision Sciences

Jason Gastelum

Pacific Northwest National Laboratory
PO Box 999
Richland, WA 99352

Biography

Jason Gastelum holds a BS and MS in Operations Research and has over ten years of experience in risk and decision analysis. As an officer in the US Air Force, Jason developed models to predict the effects of high power microwaves on electronics, and to predict the success of candidates in pilot training. Since joining the Energy and Environment Directorate at Pacific Northwest National Laboratory in 2008, Jason has applied decision and risk analyses techniques to domains including environmental remediation, commercial power plants, carbon sequestration, and the nuclear fuel market. Jason was a key contributor to a probabilistic risk assessment of TVA’s Raccoon Mountain pumped storage power plant and has contributed to several revisions NUREG 1307, which updates the waste burial costs associated with decommissioning a nuclear reactor. His other research interests include optimization using mathematical programming and discrete event simulation.

Education and Credentials

  • M.S., Operations Research with focus in Decision Analysis, Air Force Institute of Technology, Dayton, OH
  • B.S., Operations Research with minor in Mathematics, United States Air Force Academy, Colorado Springs, CO.

PNNL Publications

2020

  • Unwin S.D., D. Calderin Morales, B.C. Simpson, C.J. Spitz, A. Veeramany, J.A. Gastelum, and C.T. Maloney. 2020. "An Improved Risk Priority Measure for the Management of Project Risk." In ASME 2020 International Mechanical Engineering Congress and Exposition IMECE2020, November 16-19, 2020, Virtual, Online, 14, Paper No: IMECE2020-23917, V014T14A019. New York, New York:ASME. PNNL-SA-152881. doi:10.1115/IMECE2020-23917

2016

  • Yonkofski C., J.A. Gastelum, E.A. Porter, L.R. Rodriguez, D.H. Bacon, and C.F. Brown. 2016. "An Optimization Approach to Design Monitoring Schemes for CO2 Leakage Detection." International Journal of Greenhouse Gas Control 47, no. 2019:233-239. PNNL-SA-110925. doi:10.1016/j.ijggc.2016.01.040

Risk and Decision Sciences

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