Raymond Holsapple

Degrees:

• B.S. Mathematics – Eastern New Mexico University, 1999
• M.S. Mathematics – Texas Tech University, 2003
• Ph.D. Mathematics – Texas Tech University, 2006

Courses:

• MATH 102 – Nature of Mathematics
• MATH 130 – College Algebra with Applications
• MATH 140 – College Algebra
• MATH 150 – Introduction to Data Science
• MATH 160 – Introduction to Statistics
• MATH 175 – Introduction to Computer Programming
• MATH 210 – Calculus and Analytic Geometry I
• MATH 211 – Calculus and Analytic Geometry II
• MATH 212 – Calculus and Analytic Geometry III
• MATH 373 – Finite Mathematics
• MATH 464 – Probability and Statistics I
• MATH 478 – Introduction to Machine Learning
• MATH 479 – Introduction to Neural Networks

Selected Publications:

[B] – book chapter, [J] journal article, [C] conference paper, [P] conference poster

• [P] J. Barber, B. Ho, N. Walker, R. Holsapple, D. Roman, J. Fiegel and B. Mertz, “Utilizing Multi-Scale Modeling and Machine Learning Approaches to Characterize the Structure-Function Relationship of the Platelet-Activating Factor Receptor,” 52nd Midwest Theoretical Chemistry Conference, Columbus, OH, June 2022.
• [P] J. Barber and R. Holsapple, “Using Neural Networks for Molecular Energy Regression,” Proceedings of the 95th Annual Meeting of the West Virginia Academy of Science, Vol. 93, No. 1, April 2021.
• [P] J. Barber and R. Holsapple, “A Machine Learning Approach for Predicting Molecular Energy,” Proceedings of the 95th Annual Meeting of the West Virginia Academy of Science, Vol. 92, No. 1, April 2020.
• [B] R. Holsapple, J. Baker and A. Matlock, The Control Handbook, “Stochastic Decision Making and Aerial Surveillance Control Strategies for Teams of Unmanned Aerial Vehicles,” CRC Press, Boca Raton, FL, 2011.
• [J] R. Holsapple and D. Kingston, “Cooperative Control of Autonomous Systems,” Editorial, International Journal of Robust and Nonlinear Control, Vol. 21, No. 12, August 2011.
• [J] D. Casbeer and R. Holsapple, “A Column Generation Approach to the Vehicle Routing Problem,” International Journal of Robust and Nonlinear Control, Vol. 21, No. 12, August 2011.
• [B] J. Baker, R. Holsapple, A. Girard, P. Chandler and M. Pachter, Optimization and Cooperative Control Strategies: Proceedings of the 8th International Conference on Cooperative Control and Optimization, “Operator Aided Decision Processes for UAVs in a Stochastic Environment,” Springer, Berlin, 2009.
• [C] S. McDonald, R. Holsapple and D. Kingston, “Optimal Waypoint Placement for Road Surveillance using Unmanned Aerial Vehicles,” Proceedings of the AIAA Guidance, Navigation and Control Conference and Exhibit, Chicago, IL, August 2009.
• [C] A. Matlock, R. Holsapple, C. Schumacher, J. Hansen and A. Girard, “Cooperative Defensive Surveillance using Unmanned Aerial Vehicles,” Proceedings of the American Control Conference, St. Louis, MO, June 2009.
• [C] R. Holsapple, J. Baker, P. Chandler, A. Girard and M. Pachter, “Autonomous Decision Making with Uncertainty for an Urban Intelligence, Surveillance and Reconnaissance (ISR) Scenario,” Proceedings of the AIAA Guidance, Navigation and Control Conference and Exhibit, Honolulu, HI, August 2008.
• [J] R. Holsapple, R. Iyer and D. Doman, “Variable Step-Size Selection Methods for Implicit Integration Schemes for ODEs,” International Journal of Numerical Analysis and Modeling, Vol. 4, No. 2, 2007.
• [C] R. Iyer, R. Holsapple and P. Chandler, “A New Method for the Computation of Motion from Image Sequences,” Proceedings of the American Control Conference, Portland, OR, June 2005.
• [J] R. Holsapple, R. Venkataraman and D. Doman, “New, Fast Numerical Method for Solving Two-Point Boundary-Value Problems,” Journal of Guidance, Control and Dynamics, Vol. 27, No. 2, March 2004.

Research Interests:

My research interests are in the areas of machine learning, algorithm development, modeling dynamical systems, and control theory. I am interested in creating novel algorithms for numerically solving systems of differential equations, especially boundary value problems. My most recent research uses supervised learning algorithms and includes the estimation of molecular energy using molecular structure, and disease classification using images.