AI research · world models · physical systems

I build AI systems for physical worlds.

I design learning systems that reason over structure, adapt across changing conditions, and stay useful when simulation meets the constraints of the real world.

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Research Depth

Generalization beyond one simulator, dataset, or control task.

My recent research sits at the intersection of model-based RL, graph neural networks, physics-informed simulation, and engineered systems. The throughline is practical: learn useful structure from complex systems and test whether it still works when conditions shift.

Selected Research

G-Dreamer Graph-structured DreamerV3 experiments for RL. HOT Dataset Simulated controllable buildings for transfer research. HVAC-SPICE In-context RL with uncertainty for zero-shot HVAC control.

Research Notes

Research writing and code releases, with a point of view.

A research Substack is in development for essays that look across papers rather than treating each one in isolation. More code and artifacts will follow as recent work moves from pre-publication to public release.

Useful Links

Beyond Research

A wider set of systems, from classrooms to sport.

Before my academic career, I competed in two Olympic Games in modern pentathlon. I have also taught and mentored in engineering and sustainability, served in governance roles, and continue to work on sustainability in sport through Racing To Zero.