About

I'm a PhD student in the Fiete Lab at MIT. My work sits between deep learning theory, computational neuroscience, and artificial life, and the question underneath all of it is what minimal structure a system needs to produce complex, adaptive computation.

In practice that means running controlled experiments on diffusion models to understand how they factorize and compose concepts, building toy connectomes shaped by local growth rules, and testing evolutionary algorithms as an alternative to gradient-based finetuning. I came to this from physics: my undergraduate work at Duke was on robust quantum control for trapped-ion computers, and I still approach problems by looking for the smallest model that captures the phenomenon.

I'm also interested in self-replication: what does a system need to make a copy of itself, and what makes the copies stable? Lenia is my current playground for that.