eventSeminar
Tim de Silva | AI Financial Advice: Supply, Demand, and Life Cycle Implications
SeminarOct 07, 202612:00 PM - 1:15 PM
October
07
2026
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This session covers the foundational knowledge of GPUs, including their architecture, functionality, and applications in computing. It provides an introduction to GPU computing through the lens of the Marlowe SuperPod and prepares learners for advanced topics such as GPU-accelerated data science and machine learning.
Location: CoDa W401
This session covers the foundational knowledge of GPUs, including their architecture, functionality, and applications in computing. It provides an introduction to GPU computing through the lens of the Marlowe SuperPod and prepares learners for advanced topics such as GPU-accelerated data science and machine learning.
Location: CoDa W401
For 9/23 and 10/7 only, sessions are at the David Packard Electrical Engineering Building, Room 101. Each session features a different Stanford speaker; talk titles are announced by the organizers.
For 9/23 and 10/7 only, sessions are at the David Packard Electrical Engineering Building, Room 101. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Abstract
Understanding how neural circuits coordinate to drive behavior and decision making is a fundamental challenge in neuroscience. Unfortunately, finding a stable link between the brain and behavior has been difficult--even when behavior is consistent, neural activity can appear highly variable. In this talk, I will discuss ways that my lab is tackling this challenge to form more robust and interpretable readouts from neural circuits. The talk will focus on our recent efforts to use self-supervised learning (SSL) to decode and disentangle neural states. In SSL, invariances are achieved by encouraging “augmentations” (transformations) of the input to be mapped to similar points in the latent space. We demonstrate how this guiding principle can be used to model populations of neurons in diverse brain regions in both macaques and rodents, and disentangle different sources of information in the neural representation of movement. Our work shows that by establishing a more stable link between the brain and behavior, we can build better brain decoders and find common neural representations of behavior across individuals.
Bio
Eva L. Dyer is an Assistant Professor in the Department of Biomedical Engineering at the Georgia Institute of Technology. Dr. Dyer’s research cuts across machine learning and neuroscience to understand how neural activity can be linked to behavior and build biomarkers of disease. Dr. Dyer’s lab derives insights from the structure and function of the brain to design new artificial intelligence systems that can learn from fewer labels and adapt to changing inputs over time.
