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biology (dynamical system)

Research

Themes & work

Current work sits where cellular state, dynamical systems, and scientific machine learning overlap.

Cell fate

Cell fate & transcriptomic dynamics

I am drawn to the question of how cells commit. The language I use is dynamical systems — attractors, basins of attraction, and vector fields — applied to RNA expression profiles. The goal is a description of cellular decision-making that is richer than a cluster label: one that says something about the forces at work, the stabilities available, and the transitions that remain possible.

Behavior

Behavioral phenotyping & machine learning

At JAX I contribute to tools that make automated mouse behavior analysis practical and genetically informative. This work involves the full stack — video, pose, classifier, genetics — and the recurring challenge is designing systems that scientists actually want to use.

Physics-ML

Physics-aware scientific machine learning

My earlier work asked whether knowing the structure of a physical system — its conservation laws, its symmetries, its phase-space geometry — makes a neural network both more accurate and more honest. The answer was yes, often dramatically so. I continue to think about what it means to build models that remain faithful to mechanism.

Selected publications

Papers

Full list on Google Scholar

Behavior & genomics

Physics-informed ML

Networks & dynamics