I build AI systems that design — fusing diffusion models, reinforcement learning, and physics-based simulation to automate and reimagine how complex engineering systems are created. From ship hulls to drug molecules, I explore the frontier where generative AI meets the physical world.
Reframing engineering design as a guided generative process — using reward-directed diffusion models to navigate complex, constrained design spaces toward physically optimal solutions.
Embedding thermodynamic constraints, fluid dynamics, and structural mechanics directly into neural architectures — surrogates and samplers that respect the laws governing the physical world.
Training autonomous agents that iteratively refine engineering geometries, from heat exchangers to offshore jackets, using multi-agent cooperative RL and shaped reward landscapes.
Developing foundation models for design that transfer across domains — marine engineering, semiconductor cooling, molecular discovery — by learning universal geometry-performance relationships.
I'm open to collaborations on generative AI for engineering, industrial partnerships, and conversations about the frontier of physics-informed machine learning.