The shape of emergence.

I seek to understand current artificial intelligence through empirical and theoretical investigation, especially aiming to explain interesting and puzzling deep learning phenomena.

I have investigated how diffusion models learn to factorize and compose concepts, revealing mechanisms of compositionality in deep generative models.

Currently, I'm exploring evolution strategies for finetuning large language models, examining how evolutionary approaches can complement traditional gradient-based methods for model adaptation and alignment.

To understand biological intelligence, I build models investigating how different computational properties emerge from structural patterns arising from cortical wiring rules.

Biological connectomes exhibit rich computational behavior despite being shaped by simple, local growth rules. Each neuron forms synapses guided by spatial gradients, cell-type-specific motifs, and plasticity constraints—yet collectively, these processes give rise to structured networks capable of memory, stability, and computation.

In contrast, artificial neural networks are typically engineered by global optimization, abstracting away the mechanisms by which structure and function co-develop.

Beyond intelligence specifically, I'm interested in understanding emergence of self-organization and self-replication in general—the very foundation of life itself.

I hope to crack the code for the necessary and sufficient conditions for self-organization and self-replication to emerge, using artificial life as a platform and evolution as a tool.

My goal is to uncover the computational and dynamical principles that make such emergence possible—and to explore whether these principles can be instantiated in artificial systems.