About
I'm an ML researcher working on training and verification systems for open-ended scientific discovery. I lead Dynamical Systems, a research lab building agentic discovery systems for self-driving labs. We build tools that connect product design, materials design, process design, simulation, lab validation, and qualification evidence into one learning loop. I'm driven to enable new ways for scientists to interact with computers, so physical discovery can move toward qualified capability at the speed of learning.
Previously, I co-founded Arc Intelligence, where we built ATLAS, a continual-learning system for production agents. That work turned deployed trajectories into reward-scored memory, inference-time guidance, and distillation data, and shaped how I think about converting behavior into training signal. Before that, I built RL environments and distributed training infrastructure at NEAR, and helped grow open-source developer ecosystems at Protocol Labs.
I've come to believe that the best coaches are great teachers, and the best teachers are great learners. I built a career in machine learning by asking questions. That started as a football coach at Ohio State, Clemson, and the Los Angeles Rams, where I learned how to teach performance under pressure and construct environments where feedback, difficulty, and trust produce growth. It later took me to early-stage investing at Emerson Collective, teaching as an Assistant Professor at NYU, and doctoral work at the University of Illinois Urbana-Champaign, where I studied learning design and how to keep learners at the edge of learnability.
The science of learning is often counter-intuitive. Inverting discovery to be open-ended means shifting from goal-oriented, hypothesis-driven methodologies to approaches that prioritize curiosity, exploration, and the continuous generation of new problems, which has been the throughline of my approach to doing meaningful work and building a better world for my daughter to grow up in.