Russ College of Engineering and Technology
Faculty
Trevor Bihl
Stocker Visiting Professor in Artificial Intelligence and Cyber Security
Electrical Engineering and Computer Science
Education
- Ph.D., Electrical Engineering, Air Force Institute of Technology, 2015
- M.S., Electrical Engineering, Ohio University
- B.S., Electrical Engineering, Ohio University
Research Interests
- Analogical learning
- Artificial intelligence
- Autonomous systems
- Biostatistics
- Computer vision
- Control systems
- Cyber Systems
- Cyber physical systems
- Cyber security
- Data mining
- Electricity theft
- Machine learning
- Neural networks
- Neuromorphic systems
- Operations research
- Planning, scheduling, and routing
- Optimization
- Power systems
- Reinforcement learning
- Remote sensing
- Spiking neural networks
- Statistics
- System identification
- Transfer learning
Biography
Before joining Ohio University as the Stocker Visiting Professor of AI, Trevor Bihl served as a senior research engineer and program manager at the Air Force Research Laboratory (AFRL), Sensors Directorate (2016–2025), where he led intramural and extramural research across TRLs 1-9 (foundational to applied) in artificial intelligence, autonomous systems, machine learning, and signal processing. Prior to AFRL, he spent a decade as a research contractor. He has also served as an adjunct professor at Shawnee State University, Wright State University's Boonshoft School of Medicine, and Marshall University.
He is a senior member of the Institute of Electrical and Electronics Engineers (IEEE), and a senior member, and former board member, of the Institute for Operations Research and the Management Sciences (INFORMS).
Journal Article, Academic Journal (7)
- Song, S., Bihl, T., Liu, J. (2025). Coulomb Force-Guided Deep Reinforcement Learning for Effective and Explainable Robotic Motion Planning. Frontiers in Robotics and AI; https://www.frontiersin.org/journals/robotics-and-ai/articles/10.3389/frobt.2025.1697155/abstract.
- Combs, K., Goble, I., Howlett, S., Adams, Y., Bihl, T. (2025). Evaluating the Tradeoff Between Analogical Reasoning Ability and Efficiency in Large Language Models. 6. IEEE Transactions on Cognitive and Developmental Systems ; 17.
- Bihl, T., Young, B., Moyer, A. (2025). Physics-Informed Feature Engineering and R2-Based Signal-to-Noise Ratio Feature Selection to Predict Concrete Shear Strength. 19. Mathematics; 13: 18.
- McCloskey, B., Cox, B., Champagne, L., Bihl, T. (2025). Benefits of using blended generative adversarial network images to augment classification model training data sets. 4. The Journal of Defense Modeling and Simulation; 22: 453-466.
- Vicente-Sola, A., Kirkland, P., Di Caterina, G., Bihl, T., Masana, M. (2025). From task-aware to task-agnostic parameter isolation for incremental learning. 5. Neural Processing Letters; 57.
- Baietto, A., Stewart, C., Bihl, T. (2025). Dataset assembly for training Spiking Neural Networks. Neurocomputing; 622.
- Vicente-Sola, A., Manna, D., Kirkland, P., Di Caterina, G., Bihl, T. (2025). Spiking Neural Networks for event-based action recognition: A new task to understand their advantage. Neurocomputing; 611: 11.
Conference Proceeding (14)
- Bihl, T., Majumder, R., Wang, Z., Karanth , A., Liu, J. (2025). Toward Low-SWaP Cognitive Agents: Neuromorphic Intelligence and FPGA-Based Deployments of Event Neural Networks. ICCV; 4715-4722. https://openaccess.thecvf.com/content/ICCV2025W/NeVi/html/Bihl_Toward_Low-SWaP_Cognitive_Agents_Neuromorphic_Intelligence_and_FPGA-Based_Deployments_of_ICCVW_2025_paper.html.
- Bihl, T. (2025). A Review of Gravity Offloading. IEEE NAECON; 6.
- Simpkins, D., Rhodes, S., Bihl, T. (2025). Developing a Modular Food Label Reader with Application to Vegan/Vegetarian Products. IEEE NAECON.
- Simpkins, D., Scheller, C., Bihl, T., Witherell, J., Miller, A. (2025). Rapid Game Development with AI Demonstration. IEEE NAECON; 6.
- Simpkins, D., Scheller, C., Bihl, T., Witherell, J., Miller, A. (2025). The Big Cheese: Jamming Bubbles and Team Building in Global Game Jam. IEEE NAECON; 2.
- Bihl, T., Boland, M., Turner, D., Sarkisian, C. (2025). Topological Data Analysis for Time Series Classification of NFL Track Data. IEEE NAECON.
- Bresciani, C., Lavazza, L., Cominelli, M., Han, L., Dong, G., Gringoli, F., Kaplan, L., Srivastava, M., Bihl, T., Blasch, E., Knutson, F., Cerutti, F. (2025). Preliminary Insights Into Resource-Constrained Neuro-Symbolic Causal Complex Event Processing. 28th International Conference on Information Fusion (FUSION); 8.
- Bihl, T., Lemming, G. (2025). Developing a Framework for Biological Intelligence Modules in Autonomous Social Robots. 22nd International Conference on Ubiquitous Robots (UR); 382-389.
- Dastranj, M., de Smet, T., Wigdahl-Perry, C., Chiu, K., Bihl, T., Boubin, J. (2025). REMIX: Real-Time Hyperspectral Anomaly Detection for Small UAVs. International Conference on Unmanned Aircraft Systems (ICUAS).
- Nagura, D., Bihl, T., Liu, J. (2025). Reinforcement Learning with Human Experience (RLHE) for Racing Car Games. ASEE; https://peer.asee.org/54684.
- Baietto, A., Bihl, T. (2025). Generative Data for Neuromorphic Computing. Kona, HI: Hawaii International Conference on System Sciences; 10.
- Carrizales, C., Zhang, F., Bihl, T. (2025). Reinforcement Learning for Adversarial Environments. Honolulu, HI: Hawaii International Conference on System Sciences; 10. https://scholarspace.manoa.hawaii.edu/items/1e994478-2c43-4f4a-b9c5-2df1095d3665.
- Combs, K., Bihl, T. (2025). The Visual Analogs of Linguistic Concepts and Their Implications on Generative AI. Kona, HI: Hawaii International Conference on System Sciences; 10. https://scholarspace.manoa.hawaii.edu/items/9aa623e2-146c-41f3-96b5-3ad57fa19a58.
- Combs, K., Bihl, T., Howlett, S., Adams, Y. (2025). Zero-shot Comparison of Large Language Models (LLMs) Reasoning Abilities on Long-text Analogies. Kona, HI: Hawaii International Conference on System Sciences; 10.