Devin Hunter

(he/him)

6th-Year Ph.D. Student in Electrical Engineering

School Email: de700090@ucf.edu

Professional Summary

6th-Year Ph.D. Student in Electrical Engineering with a concentration in Robotics & Autonomous Systems at the University of Central Florida.

🎓 Expected Graduation: Summer 2027

🔬 Research Identity: Learning-Based State Estimation and Control of Uncertain Dynamical Systems

Download Resume

Education

Ph.D. Electrical Engineering

2021-08-01
2027-06-30

University of Central Florida

M.S. Electrical Engineering

2021-08-16
2025-08-01

University of Central Florida

B.S. Mathematics

2017-08-01
2021-05-08

Edward Waters University

Interests

Learning-Based State Estimation Learning-Based Control Neural Processes Uncertainty Quantification Reinforcement Learning Robotics & Autonomous Systems Multi-Agent Systems Aerospace Autonomy

📚 My Research

My research focuses on developing learning-based state estimation and control algorithms for uncertain dynamical systems operating in safety-critical environments. I am particularly interested in probabilistic machine learning, neural processes, uncertainty quantification, reinforcement learning, multi-agent systems, and physics-informed AI for robotics and aerospace autonomy.

My current work explores how machine learning can be integrated with physical models/known heuristics and formal uncertainty guarantees to enable robust decision-making, state estimation, and autonomous control in complex real-world systems. Applications of this research include autonomous robotics, informed remote sensing, distributed multi-agent coordination, and next-generation aerospace missions.

I am always interested in discussing research ideas, potential collaborations, and opportunities at the intersection of machine learning, robotics, control theory, and aerospace systems. Please feel free to reach out if you would like to connect.

Academic Publications

• Hunter, D., Augsburger, J., & O’Rourke, S. (2026). Multi-Time Scale State Estimation of Vehicle Dynamics Using Physics-Informed Neural Process. SPIE Defense + Security 2026.

• Hunter, D., & Enyioha, C. (2026). Real-Time Performance Analysis of Multi-Fidelity Residual Physics-Informed Neural Process-Based State Estimation for Robotic Systems. International Conference on Robotics, Control and Automation (ICRCA).

• Hunter, D., & Enyioha, C. (2026). Neural Process-Based Reactive Controller for Autonomous Racing. IEEE SoutheastCon 2026.

• Hunter, D., & Enyioha, C. (2025). Hybrid State Estimation of Uncertain Nonlinear Dynamics Using Neural Processes. Springer Nature Machine Learning Journal (Under Review).

• Hunter, D., & Bowers, C. (2020). Quantifying the Difference of Time Complexity Between Two Probability Algorithms. Edward Waters College’s Undergraduate Journal on Research Across the Disciplines.

Presentations & Posters

SPIE Defense + Security Poster

Poster presentation introducing a Physics-Informed Attentive Neural Process framework for multi-time-scale vehicle state estimation using remote sensing data.

Apr 28, 2026 • 1 min read

Real-Time Performance Analysis of Multi-Fidelity Residual Physics-Informed Neural Process-Based State Estimation for Robotic Systems

Conference presentation on the Multi-Fidelity Residual Physics-Informed Neural Process (MFR-PINP), a novel architecture applied for real-time multi-fidelity dynamic reconstruction. …

Mar 27, 2026 • 1 min read