Rate My Professor John Sheppard

John Sheppard is the Norm Asbjornson College of Engineering Distinguished Professor of Computer Science at Montana State University’s Gianforte School of Computing, a position he has held since 2015. Previously, he served as the RightNow Technologies Distinguished Professor from 2008 to 2015. He earned a B.S. in Computer Science (magna cum laude) from Southern Methodist University in 1983, an M.S. in Computer Science from The Johns Hopkins University in 1990, and a Ph.D. in Computer Science from The Johns Hopkins University in 1997, with a dissertation on “Multiagent Reinforcement Learning in Markov Games.” Prior to his academic career, Sheppard worked for over 20 years at ARINC Incorporated, advancing to Fellow, where he led research in data mining, cognitive modeling, and health management for defense and aerospace applications. He has been a Lecturer at Johns Hopkins University’s Engineering for Professionals program since 1994, teaching courses in artificial intelligence, machine learning, and evolutionary computation.

Sheppard’s research specializes in machine learning, probabilistic graphical models, deep learning, evolutionary and swarm-based algorithms, distributed optimization, and applications to system-level test, diagnosis, predictive health management, prostate cancer diagnosis, precision agriculture, and wildfire management. He has published over 200 peer-reviewed papers and edited three books, including Realizing Complex Integrated Systems (CRC Press, 2025), Research Perspectives and Case Studies in System Test and Diagnosis (Kluwer, 1998), and System Test and Diagnosis (Kluwer, 1994). Key publications include “Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation” (IEEE Transactions on Neural Networks and Learning Systems, 2023) and “Factored Evolutionary Algorithms” (IEEE Transactions on Evolutionary Computation, 2017). Elected an IEEE Fellow in 2007 for contributions to system-level diagnosis and prognosis, he has received the Montana State University Provost’s Award for Graduate Research and Creativity Mentoring (2022), Computer Science Department Excellence awards, and numerous best paper awards at IEEE AUTOTESTCON. Sheppard chairs IEEE standards working groups P2848 on Prognostics and Health Management and P1232 on System Diagnostic Data and Services, and participates in P2976 on eXplainable AI. He delivered the Provost Distinguished Lecture in 2021.