A bit more about me.

I'm a PhD student in the Fiete Lab at MIT, where I study how intelligence emerges — in artificial systems, in brains, and in the strange territory where life learns to organize itself. My work sits between deep learning theory, computational neuroscience, and artificial life, and the through-line is a single question: what minimal structure gives rise to complex, adaptive computation?

Most days that means running controlled experiments on diffusion models to understand how they factorize and compose concepts; building toy connectomes shaped by local growth rules; or tinkering with evolutionary algorithms as an alternative to gradient-based fine-tuning. I came to this from physics — my undergraduate work at Duke was on robust quantum control for trapped-ion computers — and I think a lot about what that physicist's instinct for "minimal models" can offer the messier sciences of mind and life.

I'm also drawn to the open-ended question of self-replication: what does a system need, exactly, to make a copy of itself, and what makes the resulting copies stable? Lenia is my current favorite playground for that.