John W. Sheppard's Publications

Patents

  1. John W. Sheppard, William R. Simpson, and Jerry L. Graham, Method and Apparatus for Diagnostic Testing Including a Neural Network for Determining Testing Sufficiency, U.S. Patent, No. 5,130,936, issued July 14, 1992, U.K. and French patents also issued.
  2. John W. Sheppard, John Gorton, Patrick Kalgren, and Liessman Sturlaugson, Integrated Model for Failure Diagnosis and Prognosis, U.S. Patent Pending, filed May 12, 2016.
  3. Joseph A. Shaw, John W. Sheppard, Bryan J. Scherrer, and Prashant Jha, Method of Herbicide-Resistant Weed Discrimination from Hyperspectral Images and Neural Networks, U.S. Provisional Patent Application No. 62/780,033, filed December 14, 2018.
  4. John W. Sheppard, Joseph A. Shaw, and Neil S. Walton, Method and System for Evaluating Produce Ripeness Using Hyperspectral Imaging and Machine Learning, U.S. Provisional Patent Application No. 62/801,882, filed February 6, 2019.

Books and Theses

  1. John W. Sheppard and William R. Simpson (eds.), Research Perspectives and Case Studies in System Test and Diagnosis, Kluwer Academic Publishers, Norwell, Massachusetts, 1998.
  2. John W. Sheppard, Multi-Agent Reinforcement Learning in Markov Games, Ph.D. Dissertation, The Johns Hopkins University, Baltimore, Maryland, 1996.
  3. William R. Simpson and John W. Sheppard, System Test and Diagnosis. Kluwer Academic Publishers, Norwell, Massachusetts, 1994.
  4. John W. Sheppard, Learning Diagnostic Information Using a Matrix-Based Approach to Knowledge Representation, Master's Project Report, The Johns Hopkins University, Baltimore, Maryland, 1989.

Journal Articles and Book Chapters

  1. Giorgio Morales and John W. Sheppard, "Dual Accuracy-Quality-Driven Neural Network for Prediction Interval Generation," re-submitted to IEEE Transactions on Neural Networks and Learning Systems, August 2023.
  2. Jordan Schupbach, Elliott Pryor, Kyle Webster, and John W. Sheppard, "A Risk-based Approach to Prognostics and Health Management Combining Bayesian Networks and Continuous Time Bayesian Networks," IEEE Instrumentation and Measurement Magazine, invited article, Vol. 26, No. 5, August 2023, pp. 3--11.
  3. Paul Hegedus, Bruce Maxwell, John W. Sheppard, Royden Loewen, Hannah Duff, Giorgio Morales, and Amy Peerlinck, "Towards a Low-Cost Comprehensive Process for On-Farm Precision Experimentation and Analysis," Agriculture 13, 524.
  4. Giorgio Morales, John W. Sheppard, Paul B. Hegedus, and Bruce D. Maxwell, "Improved Yield Prediction of Winter Wheat Using a Novel Two-Dimensional Deep Regression Neural Network," Sensors, 23(1):489, January 2023.
  5. Md. Asaduzzaman Noor, John Sheppard, and Sean Yaw, "Mixing Grain to Improve Profitability in Winter Wheat using Evolutionary Algorithms," SN Computer Science Journal, 3, 172 (2022). (Invited paper)
  6. Giorgio Morales, John W. Sheppard, Riley Logan, and Joseph A. Shaw, "Hyperspectral Dimensionality Reduction based on Inter-Band Redundancy Analysis and Greedy Spectral Selection," Remote Sensing 13, 3649, September 2021.
  7. Riley Logan, Bryan Scherrer, Jacob Senecal, Neil Walton, Amy Peerlinck, John Sheppard, and Joseph Shaw, "Assessing Produce Ripeness Using Hyperspectral Imaging and Machine Learning," Journal of Applied Remote Sensing, 15(3) 034505, 2021.
  8. Giorgio Morales, John W. Sheppard, Bryan Scherrer, Joseph A. Shaw, "Reduced-Cost Hyperspectral Convolutional Neural Networks," Journal of Applied Remote Sensing, 14(3)036519, 2020.
  9. Bryan Scherrer, John Sheppard, Prashant Jha, and Joseph Shaw, "Hyperspectral Imaging and Neural Networks to Classify Herbicide-Resistant Weeds," Journal of Applied Remote Sensing, 13(4)044516, 2019.
  10. 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, Björn 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.
  11. Logan Perrault and John Sheppard, "Compact Structures for Continuous Time Bayesian Networks," International Journal of Approximate Reasoning, Vol. 109, June 2019, pp. 19-41.
  12. John Sheppard and Shane Strasser, "Multiple Fault Diagnosis Using Factored Evolutionary Algorithms," IEEE Instrumentation and Measurement Magazine, Vol. 21, No. 4, August 2018, pp. 27-38.
  13. Kaveh Dehghanpour, M. Hashem Nehrir, John W. Sheppard, and Nathan C. Kelly, "Agent-Based Modeling of Retail Electrical Energy Markets with Demand Response," IEEE Transactions on Smart Grid, Volume 9, Issue 4, July 2018.
  14. Logan Perreault, Monica Thornton, John Sheppard, and Joseph DeBruycker, "Disjunctive Interaction in Continuous Time Bayesian Networks," International Journal of Approximate Reasoning, special FLAIRS 2016 issue on Uncertain Reasoning, Vol. 90, 2017, pp. 253-271.
  15. Liessman Sturlaugson, Logan Perreault, and John Sheppard, "Factored Performance Functions and Decision Making in Continuous Time Bayesian Networks," Journal of Applied Logic, special issue on Uncertain Reasoning, Vol. 22, July 2017, pp. 28-45.
  16. Shane Strasser, John Sheppard, Nathan Fortier, and Rollie Goodman, "Factored Evolutionary Algorithms," IEEE Transactions on Evolutionary Computation, Vol. 21, No. 2, April 2017, pp. 281-293.
  17. Kaveh Dehghanpour, M. Hashem Nehrir, John W. Sheppard, and Nathan Kelly, "Agent-Based Decision Making in Electrical Energy Markets Using Dynamic Bayesian Networks and Sparse Bayesian Learning," IEEE Transactions on Power Systems, Vol. 31, No. 6, November 2016, pp. 4744-4754.
  18. Liessman Sturlaugson and John W. Sheppard, "Uncertain Evidence in Continuous Time Bayesian Networks," International Journal of Approximate Reasoning, Vol. 70, March 2016, pp. 99-122.
  19. Caisheng Wang, Carol J Miller, M Hashem Nehrir, John W Sheppard, Shawn P McElmurry, "A Load Profile Management Integrated Power Dispatch Using a Newton-Like Particle Swarm Optimization Method," Sustainable Computing: Informatics and Systems, Special Issue on a Greener Water/Energy/Emissions, Vol. 8, December 2015, pp. 8-17.
  20. Houston King, Nathan Fortier, and John Sheppard, “An AI-ESTATE Conformant Interface for Net-Centric Diagnostic and Prognostic Reasoning,” IEEE Instrumentation and Measurement Magazine, Vol. 18, No. 4, August 2015, pp. 18-24.
  21. Liessman Sturlaugson and John Sheppard, “Sensitivity Analysis of Continuous Time Bayesian Network Reliability Models,” SIAM/ASA Journal of Uncertainty Quantification 3(1), 2015, pp. 346-369.
  22. Nathan Fortier, John Sheppard, and Shane Strasser, “Abductive Inference in Bayesian Networks using Distributed Overlapping Swarm Intelligence,” Soft Computing, 19(4):981-1001, April 2015.
  23. Patrick J. Donnelly and John W. Sheppard, “Classification of Monophonic Musical Instruments Using Bayesian Networks,” Computer Music Journal, 37(4):70-86, December 2013.
  24. Shane Strasser and John W. Sheppard, “Diagnostic Model Maturation,” IEEE Aerospace and Electronic Systems Magazine, Vol. 28, Issue 1., January 2013, pp. 34-43.
  25. Michael Schuh, John W. Sheppard, Shane Strasser, Rafal Angryk, and Clemente Izurieta, “A Visualization Tool for Knowledge Discovery in Maintenance Event Sequences,” IEEE Aerospace and Electronic Systems Magazine, Vol. 28, Issue 7, July 2013, pp. 30-39.
  26. Jesse Berwald, Tomas Gedeon, and John Sheppard, "Using Machine Learning to Predict Catastrophes in Dynamical Systems," Journal of Applied Computational Mathematics, Vol. 236, Issue 9, March 2012, pp. 2235-2245.
  27. Brian Haberman and John W. Sheppard, "Overlapping Particle Swarms for Energy-Efficient Routing in Sensor Networks," Wirelesss Networks, Online First, Springer, December 2011.
  28. Patrick Donnelly and John Sheppard, "Evolving Four-Part Harmony Using Genetic Algorithms," Applications of Evolutionary Computation Lecture Notes in Computer Science, LNCS 6625, Spring 2011, pp. 273-282.
  29. John W. Sheppard, Timothy J. Wilmering, and Mark A. Kaufman, "IEEE Standards for Prognostics and Health Management," IEEE Aerospace and Electronic Systems Magazine,, reprinted from IEEE AUTOTESTCON, Vol. 24, No. 9, September 2009, pp. 34-41.
  30. Kihoon Choi, Satnam Singh, Anuradha Kodali, Krishna Pattipati, John Sheppard, Setu Madhavi Namburu, Shunsuke Shigua, Danil Prokhorov, and Liu Qiao, "Novel Classifier Fusion Approaches for Fault Diagnosis in Automotive Systems," IEEE Transactions on Instrumentation and Measurement, Vol. 58, No. 3, March 2009, pp. 602-611.
  31. John W. Sheppard, "Guest Editorial: Special Section on the 2007 IEEE AUTOTESTCON,," IEEE Transactions on Instrumentation and Measurement, Vol. 58, No. 2, February 2009, pp. 238–239.
  32. 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.
  33. John W. Sheppard and Stephyn G. W. Butcher, “A Formal Analysis of Fault Diagnosis with D-Matrices,” Journal of Electronic Testing: Theory and Applications, 2007.
  34. John W. Sheppard and Mark A. Kaufman, “A Bayesian Approach to Diagnostics and Prognostics from Built In Test,” IEEE Transactions on Instrumentation and Measurement, special issue on Built In Test, Vol. 54, No. 3, June 2005.
  35. John W. Sheppard and William R. Simpson, “Accurate Diagnosis through Conflict Management," in Research Perspectives and Case Studies in System Test and Diagnosis, Kluwer Academic Publishers, Norwell, Massachusetts, 1998.
  36. Lee A. Shombert and John W. Sheppard, “A Behavior Model for Next Generation Test Systems,” Journal of Electronic Testing: Theory and Applications, Vol. 13, No. 3, December 1998, pp. 299–314.
  37. John W. Sheppard, “Co-Learning in Differential Games,” Machine Learning, Special Issue on Multi-Agent Learning, Vol. 33, No. 2/3, November/December 1998, pp. 201–233.
  38. John W. Sheppard, “Inducing Diagnostic Inference Models from Case Data,” in Research Perspectives and Case Studies in System Test and Diagnosis, Kluwer Academic Publishers, Norwell, Massachusetts, 1998.
  39. John W. Sheppard and William R. Simpson, “Managing Conflict in System Diagnosis,” IEEE Computer, Vol. 31, No. 3, March 1998.
  40. John W. Sheppard and Steven L. Salzberg, “A Teaching Strategy for Memory-Based Control,” Artificial Intelligence Review, Special Issue on Lazy Learning, Vol. 11, pp. 343–370, 1997. Also appears as chapter of book titled Lazy Learning.
  41. John W. Sheppard and Gerald C. Hadfield, “The Object-Oriented Design of Intelligent Test Systems,” CrossTalk: The Journal of Defense Software Engineering, August 1994 (reprinted from AUTOTESTCON 93).
  42. William R. Simpson and John W. Sheppard, “Fault Isolation in an Integrated Diagnostics Environment,” IEEE Design and Test of Computers, Vol. 10, No. 1, 1993.
  43. John W. Sheppard and William R. Simpson, “Performing Effective Fault Isolation in Integrated Diagnostics,” IEEE Design and Test of Computers, Vol. 10, No. 2, 1993.
  44. John W. Sheppard and William R. Simpson, “Applying Testability Analysis for Integrated Diagnostics,” IEEE Design and Test of Computers, Vol. 9, No. 3, September 1992.
  45. Arnold G. Blair, John W. Sheppard, and William R. Simpson, “CALS and Computer Diagnostic Aids: A Partnership for System Support,” CALS Journal, Vol. 1, No. 1, Spring 1992.
  46. Larry V. Kirkland, John W. Sheppard, and William R. Simpson, “Evaluating AI-ESTATE Standards Compliance Using a Functional Intelligence Ratio,” CrossTalk: The Journal of Defense Software Engineering, No. 39, December 1992.
  47. John W. Sheppard and William R. Simpson, “Expert Systems for Diagnostic Testing,” Avionics Magazine, Vol. 16, No. 10, October 1992, pp. 47-52.
  48. Arnold Blair, William R. Simpson, and John W. Sheppard, “A Partnership for Systems Support: Artificially Intelligent Maintenance Aids and CALS,” Logistics Spectrum, Vol. 26, Issue 3, Summer 1992, pp. 19-26.
  49. William R. Simpson and John W. Sheppard, “System Testability Assessment for Integrated Diagnostics,” IEEE Design and Test of Computers, Vol. 9, No. 1, 1992.
  50. John W. Sheppard and William R. Simpson, “Information Fusion and the Diagnosis of Avionics Systems,” Invited article for The ITEA Journal of Test and Evaluation, Vol. XII, No. 3, 1991.
  51. William R. Simpson and John W. Sheppard, “Information Fusion and the Testability of Avionics Systems,” Invited article for The ITEA Journal of Test and Evaluation, Vol. XII, No. 2, 1991.
  52. John W. Sheppard and William R. Simpson, “A Mathematical Model for Integrated Diagnostics,” IEEE Design and Test of Computers, Vol. 8, No. 4, 1991.
  53. William R. Simpson and John W. Sheppard, “System Complexity and Integrated Diagnostics,” IEEE Design and Test of Computers, Vol. 8, No. 3, 1991.

Refereed Conference and Workshop Papers

  1. Amy Peerlinck and John W. Sheppard, "Managing Objective Archives for Solution Set Reduction in Many-Objective Optimization," Proceedings of the IEEE Symposium Series on Computational Intelligence (SSCI), Mexico City, Mexico, December 2023.
  2. Md. Asaduzzaman Noor, John W. Sheppard, and Jason Clark, "Finding Potential Research Collaborations from Social Networks Derived from Topic Models," Proceedings of the IEEE International Conference on Behavioral and Social Computing (BESC), Larnyca, Cyprus, October 2023.
  3. John W. Sheppard, David Carey, Ion Neag, and Eric Gould, "A Standard for Prognostics and Health Management in the Context of Automatic Test Systems," IEEE AUTOTESTCON Conference Record, August 2023.
  4. 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.
  5. Giorgio Morales and John W. Sheppard, "Counterfactual Explanations of Neural Network-Generated Response Curves," Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), June 2023.
  6. 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.
  7. 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.
  8. Scott Wahl and John W. Sheppard, "Approximate Orthogonal Spectral Autoencoders for Community Analysis in Social Networks," Proceedings of the 21st IEEE International Conference on Machine Learning and Applications (ICMLA), December 2022.
  9. Jordan Schupbach, Elliott Pryor, Kyle Webster, and John W. Sheppard, "Combining Dynamic Bayesian Networks and Continuous Time Bayesian Networks for Diagnostic and Prognostic Modeling," {IEEE AUTOTESTCON Conference Record, August 2022, winner of the Oscar W. Sepp Best Paper Award.
  10. Amy Peerlinck and John W. Sheppard, "Addressing Sustainability in Precision Agriculture via Multi-Objective Factored Evolutionary Algorithms," to appear in Proceedings of the 14th Metaheuristics International Conference (MIC), July 2022.
  11. Amy Peerlinck and John W. Sheppard, "Multi-Objective Factored Evolutionary Optimization and the Multi-Objective Knapsack Problem," to appear in Proceedings of the IEEE Congress on Evolutionary Computation (CEC), June 2022.
  12. Kyle Webster and John W Sheppard, "Robust Spectral Based Compression of Hyperspectral Images using LSTM Autoencoders," to appear in Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), June 2022.
  13. Elliott Pryor, Amy Peerlinck, and John Sheppard, "A Study in Overlapping Factor Decomposition for Cooperative Co-Evolution," Proceedings of the IEEE Swarm Intelligence Symposium (SIS), Orlando, FL, December 2021.
  14. Na'Shea Wiesner, John Sheppard, and Brian Haberman, "Using Particle Swarm Optimization to Learn a Lane Change Model for Autonomous Vehicle Merging," Proceedings of the IEEE Swarm Intelligence Symposium (SIS), Orlando, FL, December 2021.
  15. Farshina Nazrul Shimim, Mohammad Alali, Hashem Nehrir, John Sheppard, Maryam Bahramipanah, and Zagros Shahooei, "Resiliency-Aware Power Management of Microgrids using Agent-based Dynamic Programming and Q-learning," Proceedings of the 10th IEEE PES Innovative Smart Grid Technologies Conference -- Asia, Brisbane, Australia, December 2021.
  16. Giorgio Morales, John Sheppard, Riley Logan, and Joseph Shaw, "Hyperspectral Band Selection for Multispectral Image Classification with Convolutional Networks," Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), July 2021.
  17. Jason Kuo and John Sheppard, "Tournament Topology Particle Swarm Optimization," Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Krakow, Poland, July 2021.
  18. 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.
  19. Md Asaduzzaman Noor and John W. Sheppard, "Evolutionary Grain Mixing to Improve Profitability in Farming Winter Wheat," Proceedings of the 24th International Conference on the Applications of Evolutionary Computation (EvoAPPS), Virtual Conference, April 2021, pp. 113-129.
  20. Jordan Schupbach, John Sheppard, and Tyler Forrester, "Quantifying Uncertainty in Neural Network Ensembles using U-Statistics," Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), Glasgow, Scotland, July 2020.
  21. Richard McAllister and John Sheppard, "Enhancing Neural Networks with Locality-Sensitive Clustering of Internal Representations," Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), Glasgow, Scotland, July 2020.
  22. 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.
  23. Scott Wahl and John Sheppard, "Legislative Vote Prediction using Campaign Donations and Fuzzy Hierarchical Communities," Proceedings of the IEEE International Conference on Machine Learning and Applications, Boca Raton, FL, December 2019.
  24. Tyler Forrester, Jacob Senecal, John Sheppard, and Mark Harris, “Continuous Time Bayesian Networks in Prognostics and Health Management of Centrifugal Pumps,” Proceedings of the Annual Conference of the PHM Society, Scottsdale, AZ, September 2019.
  25. Neil S. Walton, John W. Sheppard, and Joseph A. Shaw, "Using a Genetic Algorithm with Histogram-Based Feature Selection in Hyperspectral Image Classification," Proceedings of the ACM Genetic and Evolutionary Computation Conference (GECCO), Prague, Czech Republic, July 2019.
  26. Richard McAllister and John Sheppard, "Exploring Transferability in Deep Neural Networks with Functional Data Analysis and Spatial Statistics," Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, July 2019.
  27. Amy Peerlinck and John Sheppard, "AdaBoost with Neural Networks for Yield and Protein Prediction in Precision Agriculture," Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, July 2019.
  28. Jacob Senecal, John Sheppard, and Joseph Shaw, "Efficient Convolutional Neural Networks for Multi-Spectral Image Classification," Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), Budapest, Hungary, July 2019.
  29. 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.
  30. Amy Peerlinck, John Sheppard, Julie Pastorino, and Bruce Maxwell, "Optimal Design of Experiments for Precision Agriculture Using a Genetic Algorithm," Proceedings of the IEEE Congress on Evolutionary Computation (CEC), Wellington, New Zealand, June 2019.
  31. Benjamin R. Mitchell and John W. Sheppard. "Spatially Biased Random Forests," Proceedings of the Florida Artificial Intelligence Research Symposium (FLAIRS), May 2018, p. 20-25, winner Best Paper Award.
  32. John W. Sheppard and Joseph D. DeBruycker, "An Investigation of Current and Emerging Standards to Support a Framework for Prognostics and Health Management in Automatic Test Systems," IEEE AUTOTESTCON Conference Record, September 2018, pp. 124-130.
  33. Scott Wahl and John Sheppard, "Association Rule Mining in Fuzzy Political Donor Communities," Proceedings of the International Conference on Machine Learning and Data Mining (MLDM), July 2018.
  34. 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, July 2018.
  35. Stephyn Butcher, John Sheppard, and Brian Haberman, "Comparative Performance and Scaling of the Pareto Improving Particle Swarm Optimization Algorithm," Proceedings of the ACM Genetic and Evolutionary Computation Conference (GECCO), July 2018.
  36. 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), July 2018.
  37. 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), July 2018.
  38. Amy Peerlinck, John Sheppard, and Bruce Maxwell, "Using Deep Learning in Yield and Protein Prediction of Winter Wheat in Precision Agriculture," Proceedings of the International Conference on Precision Agriculture, May 2018.
  39. Bruce Maxwell, Paul Hegedus, Philip Davis, Anton Beckerman, Robert Payn, John Sheppard, Nicholas Silverman, and Clemente Izurieta, "Can Optimziation Associated with On-Farm Experimentation Using Site-Specific Technologies Improve Producer Management Decisions?", Proceedings of the International Conference on Precision Agriculture, May 2018.
  40. Richard McAllister and John Sheppard, "Evaluating Spatial Generalization of Deep Learning in Wind Vector Determination," Proceedings of the Florida Artificial Intelligence Symposium (FLAIRS), May 2018.
  41. Richard McAllister and John W. Sheppard, "Deep Learning for Wind Vector Determination," Proceedings of the IEEE Deep Learning Symposium, December 2017.
  42. Shane Strasser and John W. Sheppard, "Evaluating Factored Evolutionary Algorithm Performance on Binary Deceptive Functions," Proceedings of the IEEE Swarm Intelligence Symposium, December 2017.
  43. Shane Strasser, John Sheppard, and Stephyn Butcher, "A Formal Approach to Deriving Factored Evolutionary Algorithm Architectures," Proceedings of the IEEE Swarm Intelligence Symposium, December 2017.
  44. John W. Sheppard and Shane Strasser, "A Factored Evolutionary Optimization Approach to Bayesian Abductive Inference for Multiple Fault Diagnosis," IEEE AUTOTESTCON Conference Record, September 2017, pp. 53-62, winner of Walter E. Peterson Best New Technology Paper Award.
  45. Logan Perreault, Seth Berardinelli, Clemente Izurieta, and John Sheppard, "Using Classifiers for Software Defect Detection," Proceedings of the ISCA 26th International Conference on Software Engineering and Data Engineering, October 2017.
  46. Scott Wahl and John Sheppard, "Fuzzy Spectral Hierarchical Communities in Evolving Political Contribution Networks," Proceedings of the International Florida Artificial Intelligence Research Society (FLAIRS) Conference, May 2017, pp. 371-376.
  47. Shane Strasser and John Sheppard, "Convergence of Factored Evolutionary Algorithms," Proceedings of the Workshop on Foundations of Genetic Algorithms (FOGA), 2017, pp. 81-94.
  48. Rollie Goodman, Monica Thornton, Shane Strasser, and John Sheppard, "MICPSO: A Method for Incorporating Dependencies into Discrete Particle Swarm Optimization," Proceedings of the IEEE Swarm Intelligence Symposium, December 2016.
  49. Logan Perreault, Monica Thornton, and John W. Sheppard, "Deriving Prognostic Continuous Time Bayesian Networks from Fault Trees," Proceedings of the Annual Conference on Prognostics and Health Management, 2016, pp. 347-356.
  50. Logan Perreault, Monica Thornton, and John W. Sheppard, "Valuation and Optimization for Performance Based Logistics Using Continuous Time Bayesian Networks," IEEE AUTOTESTCON Conference Record, winner of Oscar W. Sepp Best Paper Award, September 2016.
  51. 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.
  52. 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.
  53. 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), July 2016, pp. 440-446.
  54. Hasari Tosun, Ben Mitchell, and John Sheppard, "Assessing Diffusion of Spatial Features in Deep Belief Networks," Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), July 2016, pp. 1625-1632.
  55. Hasari Tosun and John Sheppard, "Fast Classifier Learning under Bounded Computational Resources using Partitioned Restricted Boltzmann Machines," Proceedings of the IEEE International Joint Conference on Neural Networks (IJCNN), July 2016, pp. 2894-2900.
  56. Logan Perreault, Shane Strasser, Monica Thornton, and John Sheppard, "A Noisy-OR Model for Continuous Time Bayesian Networks," Proceedings of the International Florida Artificial Intelligence Research Society (FLAIRS) Conference, May 2016, pp. 668-673.
  57. Logan Perreault, Monica Thornton, Rollie Goodman, and John Sheppard, “A Swarm-Based Approach to Learning Phase-Type Distributions for Continuous Time Bayesian Networks,” Proceedings of the IEEE Swarm Intelligence Symposium (SIS), December 2015.
  58. Patrick J. Donnelly and John W. Sheppard, “Cross-Dataset Validation of Feature Sets in Musical Instrument Classification,” in Proceedings of the ICDM Workshop on Big Media Data: Understanding, Search, and Mining, November 2015.
  59. Logan Perreault, Monica Thornton, Shane Strasser, and John Sheppard, “Deriving Prognostic Continuous Time Bayesian Networks from D-matrices,” in IEEE AUTOTESTCON Conference Record, November 2015.
  60. Benjamin Mitchell, Hasari Tosun, and John Sheppard, “Deep Learning Using Partitioned Data Vectors,” Proceedings of the International Joint Conference on Neural Networks, July 2015.
  61. Nathan Fortier, John Sheppard, and Shane Strasser, “Parameter Estimation in Bayesian Networks Using Overlapping Swarm Intelligence,” Proceedings of Genetic and Evolutionary Computation Conference (GECCO), July 2015, pp. 9-16.
  62. Liessman Sturlaugson and John Sheppard, “The Long Run Behavior of Continuous Time Bayesian Networks,” Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), July 2015, pp. 842-851.
  63. Scott Wahl and John Sheppard, “Hierarchical Fuzzy Spectral Clustering in Social Networks Using Spectral Characterization,” Proceedings of the Florida Artificial Intelligence Symposium (FLAIRS), May 2015, pp. 305-310.
  64. Logan Perreault, Mike Wittie, and John Sheppard, “Communication-Aware Distributed PSO for Dynamic Robotic Search,” Proceedings of IEEE Swarm Intelligence Symposium (SIS), December 2014, pp. 65-72.
  65. Nathan Fortier and John Sheppard, “Learning Bayesian Classifiers Using Overlapping Swarm Intelligence,” Proceedings of IEEE Swarm Intelligence Symposium (SIS), December 2014, pp. 205-212.
  66. Hasari Tosun and John Sheppard, “Training Restricted Boltzmann Machines with Overlapping Partitions,” Proceedings of the European Conference on Machine Learning-Principles and Practice of Knowledge Discovery in Databases (ECML-PKDD), September 2014, pp. 195-208.
  67. Houston King, Nathan Fortier, and John Sheppard, “An AI-ESTATE Conformant Interface for Net-Centric Diagnostic and Prognostic Reasoning,” IEEE AutoTest Conference Record, runner-up Best Student Paper Award, September 2014.
  68. Houston King, Logan Perreault, and John Sheppard, “Using Continuous-Time Bayesian Networks for Standards-Based Diagnostics and Prognostics,” IEEE AutoTest Conference Record, September 2014.
  69. Liessman Sturlaugson and John Sheppard, “Inference Complexity in Continuous Time Bayesian Networks,” Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), July 2014, pp. 772-729.
  70. Patrick J. Donnelly and John W. Sheppard, “Clustering Spectral Filters for Extensible Feature Extraction in Musical Instrument Classification,” Proceedings of the Florida Artificial Intelligence Symposium (FLAIRS), May 2014, pp. 37-42.
  71. Liessman Sturlaugson and John W. Sheppard, “Factored Performance Functions with Structural Representation in Continuous Time Bayesian Networks,” Proceedings of the Florida Artificial Intelligence Symposium (FLAIRS), May 2014, pp. 512-517.
  72. Nicholas J. Ryhajlo, Liessman Sturlaugson, and John Sheppard, “Diagnostic Bayesian Networks with Fuzzy Evidence,” IEEE AUTOTESTCON Conference Record, Schaumburg, IL, September 2013, winner of Best Student Paper Award.
  73. Liessman Sturlaugson, Nathan Fortier, Patrick Donnelly, and John Sheppard, “Implementing AIESTATE with Prognostic Extensions in Java,” IEEE AUTOTESTCON Conference Record, Schaumburg, IL, September 2013, runner-up Best Student Paper Award.
  74. Nathan Fortier, John Sheppard, and Karthik Ganesan Pillai, “Bayesian Abductive Inference using Overlapping Swarm Intelligence,” Proceedings of the IEEE Swarm Intelligence Symposium (SIS), April 2013, pp. 263-270.
  75. Rachel Green and John Sheppard, “Comparing Frequency- and Style-Based Features for Twitter Author Identification,” Proceedings of the Florida Artificial Intelligence Symposium (FLAIRS), May 2013, pp. 64-69.
  76. Timothy Wylie, John Sheppard, Michael Schuh, and Rafal Angryk, “Cluster Analysis for Optimal Indexing,&rdquo Proceedings of the Florida Artificial Intelligence Symposium (FLAIRS), May 2013, pp. 166-171.
  77. Liessman Sturlaugson and John Sheppard, “Principal Component Analysis Preprocessing with Bayesian Networks for Battery Capacity Estimation,” Proceedings of the IEEE International Instrumentation and Measurement Conference (I2MTC), May 2013, pp. 98-101.
  78. Shane Strasser and John Sheppard, “An Empirical Evaluation of Bayesian Networks Derived from Fault Trees,” Proceedings of the IEEE Aerospace Conference, March 2013.
  79. Richard McAllister, John W. Sheppard, and Rafal Angryk, "Taxonomic Dimensionality Reduction in Bayesian Text Classification," Proceedings of the International Conference on Machine Learning Applications, December 2012, pp. 508-513.
  80. Benjamin Mitchell and John Sheppard, "Deep Structure Learning: Beyond Connectionist Approaches," Proceedings of the International Conference on Machine Learning Applications, December 2012, pp. 162-167.
  81. Nathan Fortier, John Sheppard, and Karthik Ganesan Pillai, "DOSI: A New Approach for Training Artificial Neural Networks Using Overlapping Swarm Intelligence," Proceedings of the International Conference on Soft Computing and Intelligence Systems and International Symposium on Advanced Intelligent Systems, November 2012, pp. 1420-1425.
  82. Karthik Ganesan Pillai and John Sheppard, "Abductive Inference in Bayesian Belief Networks Using Swarm Intelligence," Proceedings of the International Conference on Soft Computing and Intelligence Systems and International Symposium on Advanced Intelligent Systems, November 2012, pp. 375-380.
  83. Patrick J. Donnelly, Liessman E. Sturlaugson, and John W. Sheppard, “A Standards-Based Approach to Gray-Scale Health Assessment Using Fuzzy Fault Trees,”, IEEE AUTOTESTCON Conference Record, Anaheim, CA, September 2012, pp. 174-181
  84. Shane Strasser, Eben Howard, and John Sheppard, “An Integrated Toolset for Ontology-Guided Knowledge Discovery and Diagnostic Maturation Using Maintenance Data,” IEEE AUTOTESTCON Conference Record, Anaheim, CA, September 2012, pp. 280-290, runner-up Best Student Paper Award.
  85. Douglas Galarus, Rafal Angryk, and John Sheppard, “Automated Weather Sensor Quality Control,” Proceedings of the Florida Artificial Intelligence Symposium (FLAIRS), 2012.
  86. Michael Schuh, Rafal Angryk, and John Sheppard, “Evolving Kernel Functions with Particle Swarm Optimization and Genetic Programming,” Proceedings of the Florida Artificial Intelligence Symposium (FLAIRS), 2012.
  87. Shane Strasser and John W. Sheppard, "Diagnostic Alarm Sequence Maturation in Timed Failure Propagation Graphs," IEEE AUTOTESTCON Conference Record, Baltimore, MD, September, 2011, pp. 158-165, winner of Best Track Paper Award--Diagnostics and Health Assessment.
  88. Mike Schuh, John Sheppard, Shane Strasser, Rafal Angryk, and Clemente Izurieta, "Ontology-Guided Knowledge Discovery Through the Generation and Visualization of Event Sequences in Maintenance Data," IEEE AUTOTESTCON Conference Record, Baltimore MD, September, 2011, pp. 279-285, winner of Best Student Paper Award.
  89. Karthik Ganesan Pillai and John W. Sheppard, "Overlapping Swarm Intelligence for Training Artificial Neural Networks," Proceedings of the Swarm Intelligence Symposium (SIS), 2011 IEEE Symposium Series on Computational Intelligence, Paris, France, April 2011.
  90. Hasari Tosun and John Sheppard, "Incorporating Evidence Into Trust Propagation Models Using Markov Random Fields," Proceedings of the 3rd International Workshop on Security and Social Networking (SESOC), IEEE International Conference on Pervasive Computing, March 21, 2011, pp. 336-342.
  91. Shane Strasser, John Sheppard, Michael Schuh, Rafal Angryk, and Clemente Izurieta, "Graph-Based, Ontology-Guided Data Mining for D-Matrix Model Maturation," Proceedings of the IEEE Aerospace Conference, Big Sky, MT, March 2011.
  92. Scott Wahl and John Sheppard, "Extracting Decision Trees from Diagnostic Bayesian Networks to Guide Test Selection," Proceedings of the Annual Conference of the PHM Society, Prognostics and Health Management Society, June 2010.
  93. 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.
  94. 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.
  95. 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 IEEE Aerospace Conference, Big Sky, MT, March 2009.
  96. John W. Sheppard, Timothy J. Wilmering, and Mark A. Kaufman, “IEEE Standards for Prognostics and Health Management,” IEEE AUTOTESTCON 2008 Conference Record, Salt Lake City, UT, September 2008, pp. 97-103.
  97. Edward Kao, Peter VanMaasdam, and John Sheppard, "Image-Based Tracking Utilizing Particle Swarms and Probabilistic Data Association," Proceedings of the IEEE Swarm Intelligence Symposium, St. Louis, MO, September 21-23, 2008.
  98. Stephyn G. W. Butcher and John W. Sheppard, "Asset-Specific Bayesian Diagnostics in Mixed Contexts," IEEE AUTOTESTCON 2007 Conference Record, Baltimore, MD, September 2007.
  99. 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.
  100. Kihoon Choi, Satnam Singh, Krishna Pattipati, John W. Sheppard, Setu Madhavi Namburu, Shunsuke Chigusa, Danil V. Prokhorov, and Lui Qiao, "Novel Classifier Fusion Approaches for Fault Diagnosis in Automotive Systems," IEEE AUTOTESTCON 2007 Conference Record, Baltimore, MD, September 2007.
  101. Mark A. Kaufman, John W. Sheppard, and Timothy J. Wilmering, "Model-Based Standards for Diagnostic and Maintenance Information Integration," IEEE AUTOTESTCON 2007 Conference Record, Baltimore, MD, September 2007.
  102. 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.
  103. Satnam Singh, Kihoon Choi, Anuradha Kodali, Krishna Pattipati, John Sheppard, Setu Madhavi Namburu, Shunsuke Chigusa, Danil V. Prokhorov, and Liu Qiao, “Dynamic Multiple Fault Diagnosis: Mathematical Formulations and Solution Techniques,” Proceedings of the 18th International Workshop on Principles of Diagnosis (DX-07), Nashville, TN, May 2007.
  104. Timothy J. Wilmering and John W. Sheppard, “Ontologies for Data Mining and Knowledge Discovery to Support Diagnostic Maturation,” Proceedings of the 18th International Workshop on Principles of Diagnosis (DX-07), Nashville, TN, May 2007.
  105. Stephen G. W. Butcher, John W. Sheppard, Mark A. Kaufman, Hanh Ha, and Craig MacDougall, “Experiments in Bayesian Diagnostics with IUID-Enabled Data,” IEEE AUTOTESTCON 2006 Conference Record, Anaheim, California, September 2006, pp. 605–614.
  106. John W. Sheppard, Stephyn G. W. Butcher, Mark A. Kaufman, and Craig MacDougall, “Not-So-Naive Bayesian Networks and Unique Identification in Developing Advanced Diagnostics,” Proceedings of the IEEE Aerospace Conference, Big Sky, Montana, March 2006.
  107. 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.
  108. John W. Sheppard and Timothy J. Wilmering, “Recent Advances in IEEE Standards for Diagnosis and Diagnostic Maturation,” Proceedings of the IEEE Aerospace Conference, Big Sky, Montana, March 2006.
  109. 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, Seattle, Washington, June 2004.
  110. Rashad Moore, John W. Sheppard, and Ashley Williams, “Multi-Agent Simulation of Airline Travel Markets,” Proceedings of the Genetic and Evolutionary Computation Conference, Seattle, Washington, June 2004.
  111. John W. Sheppard, “System Prognostics with Non-Linear Time Series Prediction: Preliminary Results,” IEEE International Workshop on System Test and Diagnosis Digest, October 2000, Atlantic City, New Jersey.
  112. 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, May 1999.
  113. John W. Sheppard, “Information Superiority through Intelligent Information Operations,” Proceedings of the Joint Aerospace Weapon System, Support, and Simulation Symposium, San Diego, California, May 1999.
  114. John W. Sheppard and William R. Simpson, “Standardized Representations of Diagnostic Models,” Proceedings of the IEEE International Conference on System, Man, and Cybernetics, La Jolla, California, October 1998.
  115. John W. Sheppard and Leslie A. Orlidge, “Artificial Intelligence Exchange and Service Tie to All Test Environments (AI-ESTATE)--A New Standard for System Diagnostics,” Proceedings of the International Test Conference, Washington, DC, October 1997.
  116. John W. Sheppard and William R. Simpson, “Improving the Accuracy of Diagnostics Provided by Fault Dictionaries,” Proceedings of the 14th VLSI Test Symposium, Princeton, New Jersey, April 1996.
  117. John W. Sheppard and Steven L. Salzberg, “Combining Genetic Algorithms with Memory-Based Reasoning,” Proceedings of the 6th International Conference on Genetic Algorithms, Pittsburgh, Pennsylvania, July 1995.
  118. John W. Sheppard and Steven L. Salzberg, “Bootstrapping Memory-Based Learning with Genetic Algorithms,” 1994 AAAI Case-Based Reasoning Workshop, Seattle, WA, August 1994.
  119. William R. Simpson and John W. Sheppard, “The Impact of Commercial Off-the-Shelf (COTS) Equipment on System Test and Diagnosis,” Proceedings of the International Test Conference, Baltimore, MD, September 1993.
  120. John W. Sheppard, “Inducing Classification Rules for Public Health Data,” Proceedings of the Second International Workshop on Multistrategy Learning, Harpers Ferry, West Virginia, May 1993.
  121. John W. Sheppard and William R. Simpson, “Elements of Machine Learning is a Field Diagnostic Maintenance Aid,” Proceedings of  the ADPA Symposium on Artificial Intelligence Applications for Acquisition Management, Logistics Management, and Personnel Management, Williamsburg, Virginia, March 1992.
  122. William R. Simpson and John W. Sheppard, “An Intelligent Approach to Automatic Test Equipment,” Proceedings of the International Test Conference, Nashville, Tennessee, October 1991.
  123. John W. Sheppard and William R. Simpson, “Using a Competitive Learning Neural Network to Evaluate Software Complexity,” Proceedings of the 1990 ACM Symposium on Personal and Small Computers, Crystal City, Virginia, March 1990.
  124. William R. Simpson, Brian A. Kelly, and John W. Sheppard, “Clinical Protocol Development: An Information Theoretic Approach,” Sixth World Congress on Medical Informatics, Part a - Beijing, China, October, 1989, Part b - Raffle City, Singapore, December, 1989.
  125. John W. Sheppard and William R. Simpson, “Functional Path Analysis: An Approach to Software Verification,” Proceedings of the 1988 ACM Computer Science Conference, Atlanta, Georgia, February 1988.

Invited Papers and Talks

  1. John W. Sheppard, "Insurance Innovation, Artificial Intelligence, \& What to Watch For In Montana," CSI Insurance Summit, Butte, MT, September 13, 2023.
  2. John W. Sheppard, "A Standard for Prognostics and Health Management in the Context of Automatic Test Systems," IEEE AUTOTESTCON 2023, paper and standards "Ask the Experts" panel discussion, August 30, 2023.
  3. John W. Sheppard, "PHM In and For ATS," IEEE AUTOTESTCON 2023, DoD Executive Plenary Panel, August 29, 2023.
  4. John W. Sheppard, "Demystifying Machine Learning through eXplainable Artificial Intelligence (XAI)," Optical Technology Center Colloquium, Montana State University, February 10, 2023.
  5. John W. Sheppard, "Analytical Engine (AI/ML) 2022 Progress," NC-1210 Annual Conference: Frontiers in On-Farm Experimentation, Corpus Christi, TX, January 2023.
  6. John W. Sheppard "Data Intensive Farm Management: A Collaborative CIG Grant Funded by NRCS-USDA," Artificial Intelligence in Agriculture Workshop, Auburn University, March 10, 2022.
  7. John W. Sheppard, "AI Fireside Chat," American Computer and Robotics Museum, with Dr. Mary Ann Cummings and Dr. Kristen Intemann, May 4, 2021.
  8. John W. Sheppard, "Safe at Every Speed: Expeditions in Complex System Health," Provost Distinguished Lecture, Montana State University, March 9, 2021. (~3000 views on Facebook and YouTube)
  9. John W. Sheppard, "The Ethics of Precision Agriculture," Panel Discussion, IEEE Workshop on Ethics And Social Implications Of Computational Intelligence, Glasgow, Scotland, July 19, 2020.
  10. John W. Sheppard, “The Big-Data Promise of PHM in an ATS Environment,” DoD/MoD’s Digital Data Transformation and the Impacts on Automatic Test Systems, DoD ATS Executive Plenary Panel, IEEE AUTOTESTCON 2018, National Harbor, MD, September 2018.
  11. Joseph DeBruycker, Jessi L. Smith, John W. Sheppard, and Dustin B. Thoman, “New Analyses for an Old Problem, Modeling Effects of an Implicit Bias Intervention in Faculty Searches Using Continuous Time Bayesian Networks,” Poster Presentation at the Society for Personality and Social Psychology Annual Convention, Atlanta, GA, March 2018.
  12. John W. Sheppard, "Risk-based PHM: Probabilistic Methods for Continuous-time Hazard Analysis and Risk Mitigation," Keynote Speaker, PHM-2017, Harbin, China, July 2017.
  13. John W. Sheppard, “Design for Test—The Integrated Diagnostics Perspective,” Invited Panelist, AUTOTESTCON 2006, Anaheim, California, September 2006.
  14. John W. Sheppard, “Information-Based Standards and Diagnostic Component Technology,” Invited Paper and Plenary Talk for 2nd IEEE International Workshop on System Test and Diagnosis, Atlantic City, New Jersey, September 1999.
  15. John W. Sheppard, “Artificial Intelligence in Diagnosis,” Invited Seminar, Institute for Information Industry, Taipei, Taiwan, February 1998.
  16. John W. Sheppard, “The Role of Information Modeling in Developing Standards,” Invited Seminar for Computer Science Department, Florida State University, March 1998.
  17. John W. Sheppard, “System Test and Diagnosis,” Invited Seminar, Institute for Information Industry, Taipei, Taiwan, February 1998.
  18. John W. Sheppard, “Is ROI Sufficient Justification for DFT?” Keynote Address, 1993 Economics of Test Workshop, Austin, Texas, May 1993.
  19. John W. Sheppard, “Testing Fully Testable Systems: A Case Study,” Invited Panel Presentation for the International Test Conference, Baltimore, Maryland, September 1993.
  20. William R. Simpson and John W. Sheppard, “Design for Testability and Diagnosis at the System Level,” Invited Paper for Proceedings of the NASA Space Operations, Applications, and Research (SOAR) Conference, Houston, Texas, August 1992.
  21. William R. Simpson and John W. Sheppard, “A Model Based Approach to System Test and Diagnosis,” Invited Presentation for the International Conference on Computer Design, Cambridge, Massachusetts, October 1992.
  22. John W. Sheppard, “System Perspective on Diagnostic Testing,” Invited Panel Presentation for the International Test Conference, Baltimore, Maryland, September 1992.
  23. Eugene A. Esker, Jean-Paul Martin, William R. Simpson, and John W. Sheppard, “Integrating Design for Testability and Automated Testing Approaches,” IEEE AUTOTESTCON ’90 Conference Record, San Antonio, Texas, September 1990.
  24. John W. Sheppard, “An Approach to Verifying Expert System Rule Bases,” Invited Paper for 1989 International Conference on Systems, Man, and Cybernetics, Boston, Massachusetts, November 1989.

Unrefereed Conference and Workshop Papers

  1. Bruce Maxwell, Paul Hegedus, Sasha Loewen, Hannah Duff, John Sheppard, Amy Peerlinck, Giorgio Morales, and Anton Bekkerman, "Decision Support from On-Field Precision Experiments," Proceedings of the International Conference on Precision Agriculture, June 2022.
  2. Giorgio Morales, John Sheppard, Amy Peerlinck, Paul Hegedus, and Bruce Maxwell, "Generation of Site-specific Nitrogen Response Curves for Winter Wheat using Deep Learning," Proceedings of the International Conference on Precision Agriculture, June 2022.
  3. Amy Peerlinck, Giorgio Morales, John Sheppard, Paul Hegedus, and Bruce Maxwell, "Optimizing Nitrogen Application to Maximize Yield and Reduce Environmental Impact in Winter Wheat Production," Proceedings of the International Conference on Precision Agriculture, June 2022.
  4. Giorgio Morales and John W. Sheppard, "Two-Dimensional Deep Regression for Early Yield Prediction of Winter Wheat," Proceedings of the SPIE Future Sensing Technologies Conference (invited), 11914, November 2021, pp. 49--63.
  5. Riley D. Logan, Bryan Scherrer, Jacob Senecal, Neil S. Walton, Amy Peerlinck, John W. Sheppard, and Joseph A. Shaw, "Hyperspectral Imaging and Machine Learning for Monitoring Produce Ripeness," Proceedings of the SPIE Defense + Commericial Sensing Conference, April 27 – May 1, 2020.
  6. Peter Lawson, Jordan Schupbach, John Sheppard, and Brittany Terese Fasy, "Persistent Homology for the Automatic Classification of Prostate Cancer Aggressiveness in Histopathology Slides," Proceedings of the SPIE Medical Imaging Conference, February 16-21, 2019, San Diego, CA.
  7. Amy Peerlinck, John Sheppard, and Bruce Maxwell, "Using Deep Learning in Yield and Protein Prediction of Winter Wheat in Precision Agriculture," Proceedings of the International Conference on Precision Agriculture, May 2018.
  8. Bruce Maxwell, Paul Hegedus, Philip Davis, Anton Bekkerman, Robert Payn, John Sheppard, Nicholas Silverman, and Clemente Izurieta, "Can Optimization Associated with On-Farm Experimentation Using Site-Specific Technologies Improve Producer Management Decisions?" Proceedings of the International Conference on Precision Agriculture, May 2018.
  9. John W. Sheppard and Mark A. Kaufman, “Bayesian Diagnosis and Prognosis Using Instrument Uncertainty,” IEEE AUTOTESTCON 2005 Conference Record, Orlando, Florida, September 2005.
  10. John W. Sheppard and Mark A. Kaufman, “Bayesian Modeling: An Amendment to the AI-ESTATE Standard,” IEEE AUTOTESTCON 2005 Conference Record, Orlando, Florida, September 2005.
  11. John W. Sheppard, “Accounting for False Indication in a Bayesian Diagnostics Framework,” IEEE AUTOTESTCON 2003 Conference Record, Anaheim, California, September 2003.
  12. John W. Sheppard and Mark A. Kaufman, “An Integrated View of Test and Diagnostic Information Standards,” IEEE AUTOTESTCON ’02 Conference Record, Huntsville, Alabama, October 2002.
  13. Antony Bartolini, John W. Sheppard, and Thomas E. Munns, “An Application of Diagnostic Inference Modeling in Vehicle Health Management,” IEEE AUTOTESTCON 01 Conference Record, Valley Forge, Pennsylvania, August 2001.
  14. John W. Sheppard and Mark A. Kaufman, “Formal Specification of Testability Metrics in P1522,” IEEE AUTOTESTCON 01 Conference Record, Valley Forge, Pennsylvania, August 2001.
  15. John W. Sheppard and Mark A. Kaufman, “IEEE 1232 and 1522 Standards,” IEEE AUTOTESTCON 2000 Conference Record, Anaheim, California, September 2000.
  16. John W. Sheppard and Mark A. Kaufman, “IEEE Test and Diagnosis Standards,” Proceedings of the 19th Digital Avionics Systems Conference, August 2000.
  17. John W. Sheppard and Amanda Jane Giarla, “Information-Based Standards and Component Technology,” IEEE AUTOTESTCON 2000 Conference Record, Anaheim, California, September 2000.
  18. John W. Sheppard and Mark A. Kaufman, “AI-ESTATE--The Next Generation,” IEEE AUTOTESTCON 99 Conference Record, San Antonio, Texas, September 1999.
  19. Mark A. Kaufman and John W. Sheppard, “P1522: A Formal Standard for Testability and Diagnosability Standards,” IEEE AUTOTESTCON 99 Conference Record, San Antonio, Texas, September 1999.
  20. John W. Sheppard and William R. Simpson, “Prototyping a Diagnostic Interface,” IEEE AUTOTESTCON ’98, Salt Lake City, Utah, August 1998.
  21. John W. Sheppard, Antony Bartolini, and Leslie A. Orlidge, “Standardizing Diagnostic Information Using IEEE AI-ESTATE,” IEEE AUTOTESTCON 97 Conference Record, Anaheim, California, September 1997.
  22. Richard L. Maguire and John W. Sheppard, “Application Scenarios for AI-ESTATE Services,” IEEE AUTOTESTCON ‘96 Conference Record, Dayton, Ohio, September 1996.
  23. William R. Simpson and John W. Sheppard, “Diagnosis: Art versus Science,” Proceedings of NEPCON West, Anaheim, California, February 1996.
  24. William R. Simpson and John W. Sheppard, “Encapsulation and Diagnosis with Fault Dictionaries,” IEEE AUTOTESTCON ‘96 Conference Record, Dayton, Ohio, September 1996.
  25. Don Gartner and John W. Sheppard, “An Experiment in Encapsulation in System Diagnosis,” IEEE AUTOTESTCON ‘96 Conference Record, Dayton, Ohio, September 1996.
  26. John W. Sheppard, “Maintaining Diagnostic Truth with Information Flow Models,” IEEE AUTOTESTCON ‘96 Conference Record, Dayton, Ohio, September 1996.
  27. John W. Sheppard and William R. Simpson, “A Systems View of Test Standardization,” IEEE AUTOTESTCON ‘96 Conference Record, Dayton, Ohio, September 1996.
  28. John W. Sheppard and Jonas Åstrand , “Modeling Diagnostic Constraints with AI-ESTATE,” IEEE AUTOTESTCON ‘95 Conference Record, Atlanta, Georgia, August 1995, winner of Best Student Paper award.
  29. John W. Sheppard and William R. Simpson, “A View of the ABBET Upper Layers,” IEEE AUTOTESTCON ‘95 Conference Record, Atlanta, Georgia, August 1995.
  30. William R. Simpson and John W. Sheppard, “Dependency Modeling Pitfalls,” IEEE AUTOTESTCON 94 Conference Record, Anaheim, CA, September 1994.
  31. John W. Sheppard and William R. Simpson, “Multiple Failure Diagnosis,” IEEE AUTOTESTCON 94, Anaheim, CA, September 1994, winner of Best Paper award.
  32. John W. Sheppard, “Standardizing Diagnostic Models for System Test and Diagnosis,” IEEE AUTOTESTCON 94 Conference Record, Anaheim, CA, September 1994.
  33. William R. Simpson and John W. Sheppard, “A Data Fusion Approach to Integrated Diagnostics,” Proceedings of the Test Facility Working Group Conference, Las Vegas, Nevada, June 1993.
  34. Jean-Luc Larraga, William R. Simpson, and John W. Sheppard, “Intelligent Automatic Test Equipment for the Improvement of Avionics Maintenance,” Proceedings of ToolDiag 93, Toulouse, France, 1993.
  35. John W. Sheppard and Gerald C. Hadfield, "The Object-Oriented Design of Intelligent Test Systems," IEEE AUTOTESTCON 93, San Antonio, TX, September 1993, pp. 235-242.
  36. William R. Simpson and John W. Sheppard, “The Multicriterion Nature of Diagnosis,” IEEE AUTOTESTCON 93, San Antonio, TX, September 1993.
  37. John W. Sheppard and William R. Simpson, “A Systems Approach to Specifying Built-in Tests,” Proceedings of the Test Facility Working Group Conference, Las Vegas, Nevada, June 1993.
  38. William R. Simpson and John W. Sheppard, “Analysis of False Alarms During System Design,” Proceedings of the 1992 National Aerospace Electronics Conference, Dayton, Ohio, May 1992.
  39. John W. Sheppard, and William R. Simpson, “Automated Production of Information Models for Use in Model-Based Diagnosis,” Proceedings of the 1992 National Aerospace Electronics Conference, Dayton, Ohio, May 1992.
  40. Larry V. Kirkland, John W. Sheppard, and William R. Simpson, “Evaluating AI-ESTATE Standards Compliance Using a Functional Intelligence Ratio,” IEEE AUTOTESTCON 92 Conference Record, Dayton, Ohio, September 1992, winner of Walter E. Peterson Award for Best New Technology Paper.
  41. John W. Sheppard, “Explanation Based Learning With Diagnostic Models,” IEEE AUTOTESTCON 92 Conference Record, Dayton, Ohio, September 1992.
  42. John W. Sheppard and William R. Simpson, “Fault Diagnosis Under Temporal Constraints,” IEEE AUTOTESTCON 92 Conference Record, Dayton, Ohio, September 1992.
  43. Russell Crowe, William R. Simpson, and John W. Sheppard, “A Hierarchical Modeling Approach to System-Level Testability and Diagnosis,” Proceedings of the ASNE Product Engineering Symposium, Louisville, Kentucky, September 1992.
  44. Leonard Haynes, Sharon Goodall, Floyd Phillips, William R. Simpson, and John W. Sheppard, “Test Strategy Component of an Open Architecture for Electronics Design and Support Tools,” IEEE AUTOTESTCON 92 Conference Record, Dayton, Ohio, September 1992.
  45. William R. Simpson and John W. Sheppard, “A Data Fusion Approach to Integrated Diagnostics,” Proceedings of the Symposium on Artificial Intelligence for Military Logistics, Williamsburg, Virginia, March 1991.
  46. William R. Simpson and John W. Sheppard, “Developing Intelligent Automatic Test Equipment,” Proceedings of the 1991 National Aerospace and Electronics Conference, Dayton, Ohio, May 1991.
  47. John W. Sheppard and William R. Simpson, “A Neural Network for Evaluating Diagnostic Evidence,” Proceedings of the 1991 National Aerospace and Electronics Conference, Dayton, Ohio, May 1991.
  48. William R. Simpson and John W. Sheppard, “Partitioning Large Diagnostic Problems,” IEEE AUTOTESTCON `91 Conference Record, Anaheim, California, September 1991.
  49. Arnold G. Blair, John W. Sheppard, and William R. Simpson, “Reducing Logistics Costs Through Improved Field Maintenance,” Society of Logistics Engineers, Proceedings of the 26th Annual International Logistics Symposium, Dallas, Texas, August 1991.
  50. John W. Sheppard and William R. Simpson, “Uncertainty Calculations in Model-Based Reasoning,” IEEE AUTOTESTCON `91 Conference Record, Anaheim, California, September 1991.
  51. William R. Simpson and John W. Sheppard, “The Application of Evidential Reasoning in a Portable Maintenance Aid,” AUTOTESTCON '90 Conference Record, San Antonio, Texas, September 1990.
  52. Eugene A. Esker, William R. Simpson, and John W. Sheppard, “An Embedded Maintenance Subsystem,” IEEE AUTOTESTCON '90 Conference Record, San Antonio, Texas, September 1990.
  53. John W. Sheppard and William R. Simpson, “Experiences with a Model-Based Approach to the Fault Detection and Isolation of Complex Systems,” Symposium on Artificial Intelligence Applications for Military Logistics, March 1990.
  54. William R. Simpson and John W. Sheppard, “Performing Effective Fault Isolation in Integrated Diagnostics: A Hierarchical Approach to System-Level Diagnostics,” Proceedings of the 9th Digital Avionics Systems Conference, Virginia Beach, VA, October 1990.
  55. John W. Sheppard and William R. Simpson, “Incorporating Model-Based Reasoning in Interactive Maintenance Aids,” Proceedings of the 1990 National Aerospace and Electronics Conference, Dayton, Ohio, May 1990.
  56. John W. Sheppard and William R. Simpson, “Integrated Diagnosis -- A Hierarchical Approach,” IEEE AUTOTESTCON '90 Conference Record, San Antonio, Texas, September 1990.
  57. William R. Simpson, John W. Sheppard, and C. Richard Unkle, “POINTER - An Intelligent Maintenance Assistant,” IEEE AUTOTESTCON '89 Conference Record, Philadelphia, Pennsylvania, September 1989