Research Focus
Multi-Agent Systems:
Autonomous coordination and decision-making architectures for distributed systems supporting path planning, task allocation, collaborative decision-making, and execution across unmanned platforms.
Computational Astrophysics:
Applying deep learning and generative AI techniques to analyze, classify, and model large, high-dimensional astrophysical datasets, with emphasis on feature discovery, uncertainty characterization, and scalable analysis pipelines. As a follow-on to prior AI-based classification of potentially habitable exoplanets, current research explores the unique observational challenges and ML/DL methodologies associated with identifying and assessing the habitability of exomoons.
Current Applied Research
Our applied research focuses on technically hard problems at the intersection of physics, computation, and autonomy, with an emphasis on prototype-driven validation.
Future Applied Research
The future applied research extends current work in AI-assisted engineering, autonomy, and elelectromagnetic systems toward emerging scientific and operational challenges.
Lunar Infrastructure:
Prototype electromagnetic lunar rail launch concepts supporting surface-to-orbit logistics, powered by lunar solar energy and integrated with space-based payload retrieval and delivery systems for orbiting platforms.
AI-Assited Engineering:
Experimenting with AI agent-based engineering workflows built around domain-specific small language models to augment scientific reasoning, accelerate modeling and trade-space exploration, and support prototype-driven design.
Hypersonic Communications:
Concept-level exploration of AI-enabled electromagnetic architectures for hypersonic vehicles, combining plasma physics, wave-plasma interaction theory, and environmental modeling to examine transient RF transmission pathways through the plasma sheath.
Contact: Gregory Koumbis, PhD
