Research interests

I am a theoretical neuroscientist and machine learning researcher focused on advancing understanding of biological and artificial neural computations. My study focuses on the interface of Neuroscience and AI, with the goal of building theoretical tools to better understand neural network dynamics as well as algorithms that investigate the parallels between biological and artificial networks. My research is driven by neural networks’ (biological and artificial) extraordinary capacity to learn and support complex computations. In collaboration with experimental neuroscientists and machine learning researchers, I employ methods from dynamical systems, statistical physics, information theory, and machine learning to address a variety of open research problems.

My research’s prime goal is to derive circuit mechanisms from neural data and anchor them in computational and theoretical principles. As a result, my investigations begin with neural recordings or connectivity datasets, and proceed in collaboration with experimental colleagues to extract principles of neural dynamics and geometry.

Short bio

I completed my undergraduate and master’s degrees in theoretical physics at Scuola Normale Superiore, with a thesis on Supersymmetrical particles at CERN’s Theoretical Division. I subsequently went on to complete a PhD at Weizmann Institute of Science under the supervision of Misha Tsodyks. I am currently a postdoctoral researcher at the University of Washington, working under Eric Shea-Brown’s supervision. In addition, I work in the Mazzucato lab at the University of Oregon. We are developing innovative tools and theoretical understanding of neural mechanisms involved in shaping neural representations across the brain.