John Sheppard

Dr. Sheppard is a Norm Asbjornson College of Engineering Distinguished Professor in the Gianforte School of Computing at Montana State University and is a former Adjunct Professor in the Computer Science Department at Johns Hopkins. His research interests include model-based and Bayesian reasoning, reinforcement learning, game theory, and fault diagnosis/prognosis of complex systems. He is a Fellow of the IEEE, elected “for contributions to system-level diagnosis and prognosis.”

Dr. Sheppard received his BS in computer science from Southern Methodist University in 1983. Later, while a full-time member of industry, he received an MS in computer science in what is now Johns Hopkins Engineering for Professionals (1990). He continued his studies and received his Ph.D. in computer science from Johns Hopkins in the day school (1997), completing a dissertation on multi-agent reinforcement learning and Markov games.

Prior to entering academia full time, Dr. Sheppard was a member of industry for 20 years. His prior position was as a research fellow at ARINC Incorporated. Dr. Sheppard became a member of the EP faculty in 1994 where he teaches courses in machine learning and population-based algorithms. He also mentors independent studies and advises several graduate students. In 2022, he received the Provost’s Award for Graduate Research and Creativity Mentoring at Montana State University, which recognizes excellence in advising MS and PhD students.

Education History

• B.S., Computer Science, Southern Methodist University
• M.S., Computer Science, The Johns Hopkins University
• Ph.D., Computer Science, The Johns Hopkins University

Work Experience

Professor, Montana State University

Publications

Books:

1. Anthony P. Ambler and John W. Sheppard, Realizing Complex Integrated Systems, CRC Press, Boca Raton, FL, CRC Press, in press, 2025.
2. John W. Sheppard and William R. Simpson (eds.), Research Perspectives and Case Studies in System Test and Diagnosis, Kluwer Academic Publishers, Norwell, Massachusetts, 1998.
3. William R. Simpson and John W. Sheppard, System Test and Diagnosis. Kluwer Academic Publishers, Norwell, Massachusetts, 1994.

Publications with JHU Students: (Taken from 50 journal and 136 conference publications, all peer-reviewed)

1. Samra Kasim and John W. Sheppard, “Cross-Domain Similarity in Domain Adaptation for Human Activity Recognition,” Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), June 2023.
2. Kordel France and John W. Sheppard, “Factored Particle Swarm Optimization for Policy Co-training in Reinforcement Learning,” Proceedings of the ACM Genetic and Evolutionary Computation Conference (GECCO), July 2023.
3. Minh Hua and John W. Sheppard, “Evolving Intertask Mappings for Transfer in Reinforcement Learning,” Proceedings of the IEEE Congress on Evolutionary Computation (CEC), July 2023.
4. Stephen Boisvert and John W. Sheppard, “Quality Diversity Genetic Programming for Learning Decision Tree Ensembles,” in Genetic Programming, Lecture Notes in Computer Science, LNCS 12691, Springer, 2021, pp. 3–18.
5. Jason Kuo and John Sheppard, “Tournament Topology Particle Swarm Optimization,” Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Krakow, Poland, July 2021.
6. Stephen Boisvert and John W. Sheppard, “Quality Diversity Genetic Programming for Learning Decision Tree Ensembles,” Proceedings of the 24th European Conference on Genetic Programming (EuroGP), Virtual Conference, April 2021, pp. 3–18.
7. Sumeet Shah and John Sheppard, “Evaluating Explanations of Convolutional Neural Network Classifications,” Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), Glasgow, Scotland, July 2020.
8. Dennis W. Dickson, Matthew C. Baker, Jazmyne L. Jackson, Mariely DeJesus-Hernandez, NiCole A. Finch, Shulan Tian, Michael G. Heckman, Cyril Pottier, Tania F. Gendron, Melissa E. Murray, Yingxue Ren, Joseph S. Reddy, Neill R. Graff-Radford, Bradley F. Boeve, Ronald C. Petersen, David S. Knopman, Keith A. Josephs, Leonard Petrucelli, Bjrn Oskarsson, John W. Sheppard, Yan W. Asmann, Rosa Rademakers, and Marka van Blitterswijk, “Extensive Transcriptomic Study Emphasizes Importance of Vesicular Transport in C9orf72 Expansion Carriers,” Acta Neuropathologica Communications, 2019, 7:150.
9. Benjamin R. Mitchell and John W. Sheppard. “Spatially Biased Random Forests,” Proceedings of the Florida Artificial Intelligence Research Symposium (FLAIRS), Sarasota, FL, May 2019, p. 20–25, winner Best Paper Award.
10. Stephyn Butcher, John Sheppard, and Brian Haberman, “Comparative Performance and Scal- ing of the Pareto Improving Particle Swarm Optimization Algorithm,” Proceedings of the ACM Genetic and Evolutionary Computation Conference (GECCO), Kyoto, Japan, July 2018 pp. 83–84.
11. Stephyn Butcher, John Sheppard, and Shane Strasser, “Information Sharing and Conflict Resolution in Distributed Factored Evolutionary Algorithms,” Proceedings of the ACM Genetic and Evolutionary Computation Conference (GECCO), Kyoto, Japan, July 2018, pp. 5–12.
12. Stephyn Butcher, John Sheppard, and Shane Strasser, “Pareto Improving Selection of the Global Best in Particle Swarm Optimization,” Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Rio de Janeiro, Brazil, July 2018, pp. 662–669.
13. Stephyn Butcher and John Sheppard, “An Actor Model Implementation of Distributed Factored Evolutionary Algorithms,” Proceedings of the GECCO Workshop on Parallel and Distributed Evolutionary Inspired Methods, Kyoto, Japan, July 2018, pp. 1276–1283.
14. Ryan Van Soelen and John Sheppard, “Using Winning Lottery Tickets in Transfer Learning for Convolutional Neural Networks,” Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, July 2019.
15. Stephyn G. W. Butcher, Shane Strasser, Jenna Hoole, Benjamin Demeo, and John W. Sheppard, “Relaxing Consensus in Distributed Factored Evolutionary Algorithms,” Proceedings of the ACM Genetic and Evolutionary Computation Conference (GECCO), July 2016, pp. 5–12.
16. Shane Strasser, Rollie Goodman, John W. Sheppard, and Stephyn G. W. Butcher, “A New Discrete Particle Swarm Optimization Algorithm,” Proceedings of the ACM Genetic and Evolutionary Computation Conference (GECCO), July 2016, pp. 53–60.
17. Shehzad Qureshi and John Sheppard, “Dynamic Sampling in Training Artificial Neural Networks with Overlapping Swarm Intelligence,” Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Vancouver, BC, July 2016, pp. 440–446.
18. Benjamin Mitchell, Hasari Tosun, and John Sheppard, “Deep Learning Using Partitioned Data Vectors,” Proceedings of the International Joint Conference on Neural Networks (IJCNN), July 2015.
19. Rachel Green and John Sheppard, “Comparing Frequency- and Style-Based Features for Twitter Author Identification,” Proceedings of the International Florida Artificial Intelligence Research Society (FLAIRS) Conference, May 2013, pp. 64–69.
20. Benjamin Mitchell and John Sheppard, “Deep Structure Learning: Beyond Connectionist Approaches,” Proceedings of the International Conference on Machine Learning Applications (ICMLA), December 2012, pp. 162–167.
21. Brian Haberman and John W. Sheppard, “Overlapping Particle Swarms for Energy-Efficient Routing in Sensor Networks,” Wireless Networks, 18(4):351–363, May 2012.
22. John W. Sheppard, Stephyn G. W. Butcher, and Patrick J. Donnelly, “Demonstrating Semantic Interoperability of Diagnostic Reasoners via AI-ESTATE,” Proceedings of the IEEE Aerospace Conference, Big Sky, MT, March 2010.
23. John W. Sheppard, Stephyn G. W. Butcher, and Patrick J. Donnelly, “Standard Diagnostic Services for the ATS Framework,” IEEE AUTOTESTCON 2009 Conference Record, Anaheim, CA, September 2009, pp. 393–400.
24. John W. Sheppard, Stephyn G. W. Butcher, Patrick J. Donnelly, and Benjamin R. Mitchell, “Demonstrating Semantic Interoperability of Diagnostic Models via AI-ESTATE,” Proceedings of the IEEE Aerospace Conference, Big Sky, MT, March 2009.
25. Stephyn G. W. Butcher and John W. Sheppard, “Distributional Smoothing in Bayesian Fault Diagnosis,” IEEE Transactions on Instrumentation and Measurement, Vol 58, No 2, February 2009, pp. 342–349.
26. Edward Kao, Peter VanMaasdam, and John Sheppard, “Image-Based Tracking Utilizing Particle Swarms and Probabilistic Data Association,” Proceedings of the IEEE Swarm Intelligence Symposium (SIS), St. Louis, MO, September 21–23, 2008.
27. John W. Sheppard and Stephyn G. W. Butcher, “A Formal Analysis of Fault Diagnosis with D-Matrices,” Journal of Electronic Testing: Theory and Applications, Vol. 23, No. 4, 2007, pp. 309–322.
28. Stephyn G. W. Butcher and John W. Sheppard, “Asset-Specific Bayesian Diagnostics in Mixed Contexts,” IEEE AUTOTESTCON 2007 Conference Record, Baltimore, MD, September 2007, pp. 113–121.
29. Stephyn G. W. Butcher and John W. Sheppard, “Improving Diagnostic Accuracy by Blending Probabilities: Some Initial Experiments,” Proceedings of the 18th International Workshop on Principles of Diagnosis (DX-07), Nashville, TN, May 2007, pp. 235–242.
30. Sean R. Martin, Steve E. Wright, and John W. Sheppard, “Offline and Online Evolutionary Bi-Directional RRT Algorithms for Efficient Re-Planning in Environments with Moving Obstacles,” Proceedings of the 3rd annual IEEE Conference on Automation Science and Engineering, New York: IEEE Press, September 2007, pp. 1131–1336.
31. Stephen G. W. Butcher, John W. Sheppard, Mark A. Kaufman, Hanh Ha, and Craig Mac- Dougall, “Experiments in Bayesian Diagnostics with IUID-Enabled Data,” IEEE AUTOTESTCON 2006 Conference Record, Anaheim, California, September 2006, pp. 605–614.
32. John W. Sheppard, Stephyn G. W. Butcher, Mark A. Kaufman, and Craig MacDougall, “Not-So-Nave Bayesian Networks and Unique Identification in Developing Advanced Diagnostics,” Proceedings of the IEEE Aerospace Conference, Big Sky, Montana, March 2006.
33. John W. Sheppard and Stephyn G. W. Butcher, “On the Linear Separability of Diagnostic Models,” IEEE AUTOTESTCON 2006 Conference Record, Anaheim, California, September 2006, pp. 626–635.
34. Brian Howard and John W. Sheppard, “The Royal Road Not Taken: A Re-Examination of the Reasons for GA Failure on R1,” Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), Seattle, Washington, June 2004.
35. Rashad Moore, John W. Sheppard, and Ashley Williams, “Multi-Agent Simulation of Air- line Travel Markets,” Proceedings of the Genetic and Evolutionary Computation Conference (GECCO), Seattle, Washington, June 2004.
36. Mike Waters and John W. Sheppard, “Genetic Programming and Co-evolution with Exogenous Fitness in an Artificial Life Environment,” Proceedings of the Congress on Evolutionary Computation (CEC), May 1999.

Honors and Awards

• MSU Provost’s Award for Graduate Research/Creativity Mentoring (2022)
• MSU Alumni Foundation Faculty/Staff Award of Excellence (2022)
• Provost Distinguished Lectureship (MSU) (2021)
• Vice President for Research Meritorious Technology/Science Award (2016)
• College of Engineering Distinguished Professor (2015)
• IEEE Fellow for contributions to system level diagnosis and prognosis (2007)
• IEEE AUTOTESTCON Frank McGinnis Professional Achievement Award (2007)

Professional Organizations

Institute for Electrical and Electronics Engineers
Prognostics and Health Management Society
Association for Uncertainty in Artificial Intelligence
Association for Computing Machinery/Special Interest Group on Genetic and Evolutionary Computation
Sigma Xi