BIDS Machine Learning and Science Forum

ML&Sci Forum

May 16, 2022
11:00am to 12:00pm
Virtual Participation

BIDS Machine Learning and Science Forum
Date: Monday, May 16, 2022
Time: 11:00 AM - 12:00 PM Pacific Time
Location: Participate remotely using this Zoom link 

Title/Abstract TBA
Speaker: Sophia Sanborn, PhD Student (Psychology) and Researcher, Redwood Center for Theoretical Neuroscience, UC Berkeley

The BIDS Machine Learning and Science Forum meets biweekly to discuss current applications across a wide variety of research domains in the physical sciences and beyond. Hosted by BIDS Affiliates Uroš Seljak (professor of Physics at UC Berkeley) and Ben Nachman (physicist at Lawrence Berkeley National Laboratory), these active sessions bring together domain scientists, statisticians, and computer scientists who are either developing state-of-the-art methods or are interested in applying these methods in their research.  To receive email notifications about upcoming meetings, or to request more information, please contact the organizers at berkeleymlforum@gmail.comAll interested members of the UC Berkeley and Berkeley Lab communities are welcome and encouraged to attend. 

Speaker(s)

Sophia Sanborn

Redwood Center for Theoretical Neuroscience, UC Berkeley

Sophia Sanborn is a PhD Student in Psychology and a Researcher at the Redwood Center for Theoretical Neuroscience at UC Berkeley. She uses machine learning and mathematics to understand computational principles in biological neural networks, and draw inspiration from biology to build machine learning systems. One of the most important problems that biological systems solve is modeling the symmetries and geometric structure of the natural world. Across sensory and motor regions in the brain, a striking property of neural circuits is that they tend to mirror the structure of systems they represent—either in the topological layout of their connections, or in the implicit manifold generated by their activity. This can be described as an equivariant functor from the world to the neural substrate. Sanborn studies the properties of such symmetry-preserving representations, in both biological and artificial neural systems.