Date: Tuesday, June 24th, 2025

Time: 10:00 AM – 12:00 PM EST

Location: Room 1224, MoSE

Hybrid Info: Teams Link, Meeting ID: 285 202 636 593 8, Passcode: Gs25WZ39

MoSE Address: 901 Atlantic Dr NW, Atlanta, GA 30332

Nearest Parking Deck: Area 5 Parking Deck, State St NW, Atlanta, GA 30332

Scott Mayberry

Robotics PhD Candidate

Woodruff School of Mechanical Engineering

Georgia Institute of Technology

Committee:

• Dr. Fumin Zhang (advisor) – School of Electronic and Computer Engineering, Hong Kong University of Science and Technology
• Dr. Jun Ueda – School of Mechanical Engineering, Georgia Institute of Technology
• Dr. Karim Sabra – School of Mechanical Engineering, Georgia Institute of Technology
• Dr. Fan Zhang – School of Mechanical Engineering, Georgia Institute of Technology
• Dr. Patricio Vela – School of Electrical and Computer Engineering, Georgia Institute of Technology

Abstract:

Environmental phenomena such as algal blooms and pollutant dispersion evolve as continuous spatiotemporal fields that demand real-time monitoring for effective management, rapid response, and policymaking. While modern sensing and modeling frameworks have leveraged satellite imagery, sensor networks, and deep learning to advance environmental monitoring, underwater environments pose unique challenges due to communication constraints, costly infrastructure, and the absence of shared research platforms. Addressing these barriers is critical for enabling accurate, timely insights in aquatic settings.

In this thesis, I co-design aquatic hardware and distributed estimation algorithms for real-time monitoring of spatiotemporal fields in resource-constrained underwater settings. I introduce an open-source testbed combining custom acoustic modems, particle-filter localization, and miniature underwater robots for decentralized sensing. I also develop a modular suite of distributed cooperative filters that embed physics constraints, perform online parameter estimation and data-driven model discovery, and deliver uncertainty-aware, physics-informed forecasts through stochastic discontinuous Galerkin methods.