Hi, I’m Grant Stagg

I’m a PhD student in Electrical & Computer Engineering at Brigham Young University, part of the MAGICC Lab. My research focuses on safe path planning and learning in uncertain, adversarial environments, with applications in UAV navigation, radar avoidance, and probabilistic engagement zone modeling.

I’m currently seeking industry or research roles related to UAV autonomy, optimization, or safety in adversarial systems.

Explore more:

• Research — academic projects, papers, and methods
• Projects — code, demos, and technical builds
• CV — background, education, and contact info

---

Publications

• Decentralized Sparse Gaussian Process Regression with Event-Triggered Adaptive Inducing Points
  Published in the Journal of Intelligent and Robotic Systems, Aug. 2023 📄 Paper

• Multi-Agent Path Planning for Level Set Estimation Using B-Splines and Differential Flatness
  Published in IEEE Robotics and Automation Letters, May 2024 📄 Paper

• Probabilistic Weapon Engagement Zones
  Presented at the 2025 American Control Conference 📄 Paper

• Bi-Level Route Optimization and Path Planning with Hazard Exploration
  Accepted for presentation at the 2025 Conference on Decision and Control (CDC) 📄 Paper

• Cooperative Multi-Agent Path Planning for Heterogeneous UAVs in Contested Environments
  Submitted to the AIAA Journal of Guidance, Control, and Dynamics (JGCD) 📄 Paper

• Turn-Rate Limited Probabilistic Weapon Engagement Zones
  Accepted for presentation at the 2026 AIAA SciTech Forum; journal version to be submitted to the AIAA Journal of Aerospace Information Systems (JAIS)

---

Technical Interests

• Trajectory optimization and nonlinear programming
• Differential programming and autodiff (JAX)
• Uncertainty quantification and probabilistic inference
• Engagement zone modeling and UAV autonomy
• Python, C++, ROS