Associate Department Head, Professor, and Southern Company Faculty Fellow
Biography
Dr. Jamie Baalis Coble is a Professor, Southern Company Faculty Fellow, and Associate Department Head in the Nuclear Engineering department at the University of Tennessee, Knoxville. Prior to joining the UT faculty, she worked in the Applied Physics group at Pacific Northwest National Laboratory. She is a fellow of the Prognostics and Health Management (PHM) Society and the International Society for Engineering Asset Management (ISEAM), a senior member of Institute of Electrical and Electronics Engineers (IEEE) and International Society of Automation (ISA), and a member of American Nuclear Society and U.S. Women in Nuclear.
Research
Data analytics, AI/ML, and digital thread for robust decision making: empirical modeling, process and equipment monitoring, anomaly detection and diagnostics, system prognostics, enhanced risk monitoring, supervisory control, operations and maintenance optimization, flexible concepts of operation, and operator and engineering support systems
Education
Ph.D., Nuclear Engineering, University of Tennessee-Knoxville, Knoxville, TN, 2010
M.S., Reliability and Maintenance Engineering (Concentration in Prognostics), University of Tennessee-Knoxville, Knoxville, TN, 2009
M.S., Nuclear Engineering, University of Tennessee-Knoxville, Knoxville, TN, 2006
B.S., Nuclear Engineering and Mathematics, University of Tennessee-Knoxville, Knoxville, TN, 2005
Professional Service
U.S. Women in Nuclear Steering Committee, Member at Large and Student Chapter Liaison, 2019-2025
American Nuclear Society Board of Directors, 2022-2025
Diversity and Inclusion in ANS (DIA) Executive Committee, 2019-2025
Associate Editor, Nuclear Technology, 2021-present
Prognostics and Health Management Society Board of Directors, Nuclear Power Sector Lead, 2022 - present
Awards and Recognitions
Outstanding Graduate Research Mentor Award, UTK Graduate Student Senate, 2024
Outstanding Service to the College Award, UTK Tickle College of Engineering, 2021
Professional Promise in Research Award, UTK Tickle College of Engineering, 2020
Faculty Service Award, UTK Nuclear Engineering Department, 2020
Professor of the Year, UTK Nuclear Engineering Department, 2019
Angie Warren Perkins Award, UTK Commission for Women, 2018
Leon and Nancy Cole Superior Teaching Award, UTK Tickle College of Engineering, 2018
ANS HFICD Ted Quinn Early Career Award, 2017
ASEE Southeastern Section New Faculty Research Award, Second Place, 2017
Best paper award, 2014 IEEE HST for “Towards a Theory of Autonomous Reconstitution of
Compromised Cyber-Systems”
PNNL Outstanding Performance Award (for work on LWRS NDE Roadmap), 2012
PNNL Outstanding Performance Award (for work on Ultrasonic Container Screening System), 2012
Publications
A full list of Coble’s publications is available at her Google Scholar Profile.
X. Chen, J. Coble, and F. Zhang. A full-scope, high-fidelity simulator-based hardware-in- the-loop testbed for comprehensive nuclear power plant cybersecurity research. Progress in Nuclear Energy, 175:105348, 2024.
E. Gursel, B. Reddy, K. Daniels, J. B. Coble, M. Madadi, V. Agarwal, R. Boring, V. Yadav, and A. Khojandi. Spidarman: system-level physics-informed detection of anomalies in reactor collected data considering human errors. Nuclear Technology:1–13, 2024.
M. Alberts, S. St. John, B. Jared, J. Karandikar, A. Khojandi, T. Schmitz, and J. Coble. Chatter detection in simulated machining data: a simple refined approach to vibration data. The International Journal of Advanced Manufacturing Technology:1–17, 2024.
A. O. Ifeanyi, D. Dos Santos, A. Saxena, and J. Coble. Fault detection and isolation in simulated batch operation of fine motion control rod drives. Nuclear Technology:1–17, 2024.
A. O. Ifeanyi, J. B. Coble, and A. Saxena. A deep learning approach to within-bank fault detection and diagnostics of fine motion control rod drives. International Journal of Prognostics and Health Management, 15(1), 2024.
M. Zanotelli, J. W. Hines, and J. B. Coble. Combining similarity measures and left- right hidden markov models for prognostics of items subjected to perfect and imperfect maintenance. Nuclear Science and Engineering, 0(0):1–15, 2024.
H. Xiao, J. Coble, and J. W. Hines. Auxiliary particle filter for prognostics and health management. International Journal of Prognostics and Health Management, 14(2), 2023.
S. Irvine, H. Andrews, K. Myhre, and J. Coble. Radiative transition probabilities of neutral and singly ionized rare earth elements (la, ce, pr, nd, sm, gd, tb, dy, ho, er, tm, yb, lu) estimated by laser-induced breakdown spectroscopy. Journal of Quantitative Spectroscopy and Radiative Transfer, 297:108486, 2023.
M. Sethu, B. Kotla, D. Russell, M. Madadi, N. A. Titu, J. B. Coble, R. L. Boring, K. Blache, V. Agarwal, V. Yadav, et al. Application of artificial intelligence in detection and mitigation of human factor errors in nuclear power plants: a review. Nuclear Technology, 209(3):276–294, 2023.
S. St John, M. Alberts, J. Karandikar, J. Coble, B. Jared, T. Schmitz, C. Ramsauer, D. Leitner, and A. Khojandi. Predicting chatter using machine learning and acoustic signals from low-cost microphones. The International Journal of Advanced Manufacturing Technology:1–16, 2023.