Hi, I am Lorenzo 👋 I am an Associate Research Scientist at the Center for Theoretical Neuroscience of the Zuckerman Institute at Columbia University in New York, where I code and decode brains for a living.

I am broadly interested in how the brain encodes and processes information, a question that I approach using data analysis, computer simulations, and mathematical modeling. My recent work focuses on how the geometrical properties of neural representations can provide a computational substrate for cognitive processes such as memory and abstraction (see, e.g., my work on social memory in the hippocampus and emotional states in the amygdala).

Before that, I mainly worked on continuous attractor networks and statistical inference models applied to neural data, specifically to investigate how memorized cognitive maps are retrieved from sensory cues and how similar environments are pattern-separated in the hippocampus.

If you are interested in neural decoding and geometry, check out Decodanda, my Python package for decoding and geometrical analysis of neural activity. You can find it on GitHub (make sure to star it to stay updated!) or read more about it in my resources section.

Before joining Columbia, I received my Ph.D. in Statistical Physics from École Normale Superieure and worked as a postdoctoral scientist at Institut Pasteur. During my graduate years, I co-founded Cubbit, a tech startup focused on distributed cloud storage (that is still going strong thanks to my cofounders who are running it!). My research at the Zuckerman Institute is currently supported by an NIMH K99 award.