John W. Sheppard, PhD, FIEEE

Contact Information

Dr. John W. Sheppard
College of Engineering Distinguished Professor and RightNow Technologies Fellow
Director, Numerical Intelligent Systems Laboratory
Gianforte School of Computing
365 Barnard Hall
Montana State University
Bozeman, MT 59717
V: +1 406 994 4835
F: +1 406 994 4376
E: john dot sheppard at montana dot edu

Biographical Sketch

Dr. John Sheppard was the inaugural RightNow Technologies Distinguished Professor in Computer Science at Montana State University and currently holds an appointment as College of Engineering Distinguished Professor in the Computer Science Department at MSU. He also an Adjunct Professor in the Department of Computer Science at Johns Hopkins University. In 2007, he was elected as an IEEE Fellow "for contributions to system-level diagnosis and prognosis." Prior to joining Hopkins, he was a Fellow at ARINC Incorporated in Annapolis, MD where he worked for almost 20 years. Dr. Sheppard performs research in Bayesian classification, dynamic Bayesian networks, evolutionary methods, and optimization. In addition, Dr. Sheppard is active in IEEE Standards activities. Currently, he serves as a member of the IEEE Computer Society Standards Activities Board and is the Computer Society liaison to IEEE Standards Coordinating Committee 20 on Test and Diagnosis for Electronic Systems. He is also the co-chair of the Diagnostic and Maintenance Control Subcommittee of SCC20 and has served as an official US delegate to the International Electrotechnical Commission's Technical Committee 93 on Design Automation.

Education

  • BS, Computer Science (magna cum laude), Southern Methodist University, 1983
  • MS, Computer Science, The Johns Hopkins University, 1990
  • PhD, Computer Science, The Johns Hopkins University, 1997

Appointments

  • College of Engineering Distinguished Professor in Computer Science, Montana State University
  • Adjunct Professor, Department of Computer Science, The Johns Hopkins University
  • Lecturer, Computer Science Program, Engineering and Applied Science Programs for Professionals, The Johns Hopkins University

Professional Activities

  • IEEE Fellow
    • IEEE Computer Society
    • IEEE Computational Intelligence Society
    • IEEE Instrumentation and Measurement Society
    • IEEE Standards Association
  • IEEE Computer Society Liaison to SCC20
  • Member-At-Large, IEEE Computer Society Standards Activities Board
  • Co-Chair, IEEE SCC20, Diagnostic and Maintenance Control Subcommittee
  • Tutorials Chair, IEEE AUTOTESTCON 2017ff
  • Technical Program Chair, IEEE AUTOTESTCON 2001, 2007, and 2011
  • Technical Program Chair, IEEE International Workshop on System Test and Diagnosis, 1998-2000
  • Associate Editor, IEEE Transactions on Instrumentation and Measurement

Research Interests

  • Machine Learning
  • Data Mining
  • Data Science
  • Bayesian Networks
  • Neural Networks
  • Deep Learning and Deep Feature Extraction
  • Evolutionary and Swarm-based Methods
  • Factored Optimization Methods
  • System-Level Fault Diagnosis
  • System-Level Fault Prognosis
  • Measurement Uncertainty

Courses Taught

  • Montana State University
    • CSCI 246: Discrete Structures
    • CSCI 440: Database Systems
    • CSCI 446: Artificial Intelligence
    • CSCI 447: Machine Learning: Soft Computing
    • CSCI 500: Seminar in Machine Learning
    • CSCI 547: Machine Learning
    • CSCI 548: Reasoning Under Uncertainty
    • CSCI 550: Data Mining
  • Johns Hopkins University
    • 600.335/435: Artificial Intelligence (Homewood)
    • 600.475: Machine Learning (Homewood)
    • 600.735: Seminar in Machine Learning (Homewood)
    • 605.621: Foundations of Algorithms (EP)
    • 605.445: Artificial Intelligence (EP)
    • 605.649: Introduction to Machine Learning (EP)
    • 605.746: Machine Learning (EP)
    • 605.747: Evolutionary Computation (EP)

Current Graduate Students

  • Tyler Forrester (PhD Advisee)
  • Mark Harris (MS Advisee)
  • Shriyansh Kothari (MS Advisee)
  • Richard McAllister, ABD (PhD Advisee)
  • Amy Peerlinck (PhD Advisee)
  • Jacob Senecal (MS Advisee)
  • Jordan Schupbach (PhD Advisee, with John Borkkowski)
  • Scott Wahl, ABD (PhD Advisee)
  • Neil Walton (MS Advisee)
  • Ross Wendt (MS Advisee)
  • Na'Shea Wiesner (PhD Advisee)
  • Lucia Williams (PhD Committee)
  • Nathan Woods (PhD Committee)

Prior PhD Students

  • Stephyn Butcher (2018), Information Sharing and Conflict Resolution in Particle Swarm Optimization Variants, now at Appsys Safety
  • Patrick Donnelly (2015), Learning Spectral Filters for Single- and Multi-Label Classification of Musical Instruments, now at California State University, Chico
  • Nathan Fortier (2015), Inference and Learning in Bayesian Networks Using Overlapping Swarm Intelligence, now at Golden Helix
  • Benjamin Mitchell (2017), The Spatial Inductive Bias of Deep Learning, now at Villanova University
  • Logan Perreault (2017), Improved Scalability and Expressiveness for Continuous Time Bayesian Networks now at Cruise Automation
  • Shane Strasser (2017), Factored Evolutionary Algorithms: Cooperative Coevolutionary Optimization with Overlap, now at Oracle
  • Liessman Sturlaugson (2014), Extensions to Modeling and Inference in Continuous Time Bayesian Networks, now at Boeing Research & Technology
  • Hasari Tosun (2016), Efficient Machine Learning Using Partitioned Restricted Boltzmann Machines, now at Turnitin