Dizon-Paradis, O. P, Wormald S. E., Capecci, D. E., Bhandarkar A., and Woodard, D. L. (2024). Resource usage evaluation of discrete model-free deep reinforcement learning algorithms. Reinforcement Learning Journal, ISBN 979-8-218-41163-3, 2024. Accepted.
Wormald et al., “Integrated Computational Materials Engineering to Predict Dimensions for Steady-State and Transient Melt-Pool Formation in the Selective Laser Melting of Inconel 625.”
Informally Published
Preprint: Wormald, S., Koblah, D. Maldaner, M. K., Forte, D., & Woodard, D. L. (2024). eXpLogic: Explaining Logic Types and Patterns in DiffLogic Networks. International Conference of Information Technology. https://doi.org/10.48550/arXiv.2503.09910
Preprint: Wormald, S., Maldaner, M. K., O'Connor, K. D., Dizon-Paradis, O. P., & Woodard, D. L. (2024). Abstracting General Syntax for XAI after Decomposing Explanation Sub Components. http://dx.doi.org/10.21203/rs.3.rs-4824427/v1
Preprint: Dizon-Paradis, O. P., Wormald, S. E., Zhu, M., Wilson, R., & Woodard, D. L. (2024). LfRLD: Learning from Reinforcement Learning Demonstrations. Authorea Preprints. https://doi.org/10.36227/techrxiv.172927301.18452996/v1
Conference Presentations
“Resource Usage Evaluation of Discrete Model-Free Deep Reinforcement Learning Algorithms,” Reinforcement Learning Conference, Amherst, MA, United States, August 2024
“Transition to Application for Feature-specific Additive Manufacturing Parameter Prediction to Reduce Defects in the As-built Material,” ASTM, ICAM International Conference on AM, November 2021.
Mentored Posters & Presentations
M. K. Maldaner, R. Valle, L. Nayab, S. Wormald, D. Forte, and D. Woodard, Accelerating Real-Time Inference with FPGA-Implemented Logic Gate Neural Networks, Florida Undergraduate Research Conference (FURC), 2025.
S. Wormald, D. Koblah, M. K. Maldaner, D. Forte, and D. Woodard, eXpLogic: Explaining Logic Types and Patterns in DiffLogic Networks, HiPerGator Symposium, 2025.
W. Bowman, S. Patel, R. Pu, S. Wormald, D. Woodard, Towards Transparent AI: Envisioning Comprehensive Frameworks for Explainability, University of Florida AI Days, 2024.
O. P. Dizon-Paradis, S. E. Wormald, D. E. Capecci, A. Bhandarkar, and D. L. Woodard, Resource Usage Evaluation of Discrete Model-Free Deep Reinforcement Learning Algorithms, Reinforcement Learning Conference (RLC), 2024.
M. K. Maldaner, S. Wormald, O. Dizon-Paradis, and D. L. Woodard, Ethical Horizons in Neuro Symbolic AI, UF Center for Undergraduate Research (CUR), Spring Undergraduate Research Symposium, 2024.
K. Ambrose, O. Dizon-Paradis, S. E. Wormald, and D. L. Woodard, Causal AI: The Frontier of Cause and Effect in AI, UF Center for Undergraduate Research (CUR), Spring Undergraduate Research Symposium, 2024.
Matheus Kunzler Maldaner, Stephen Wormald, Olivia Dizon-Paradis, Damon L. Woodard, Neuro Symbolic AI: Merging Neural Networks and Symbolic Reasoning for AI Transparency, Florida Undergraduate Research Conference (FURC), 2024.
(Submitted) K. O'Connor, S. Wormald, and D. Woodard, Causal Clusters: Representing Explainable Feature Learned by Deep Neural Networks as Causal Graphs, UF Center for Undergraduate Research (CUR), Spring Undergraduate Research Symposium, 2025.
(Submitted) E. Bloomquist, A. Cole, S. Wormald, and D. Woodard, From Black-Box to Glass-Box Models: Toward Explainability in Reinforcement Learning, UF Center for Undergraduate Research (CUR), Spring Undergraduate Research Symposium, 2025.
