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.
Dr. Kendrick Kay is an Associate Professor at the Center for Magnetic Resonance Research at the University of Minnesota. Research in his Computational Visual Neuroscience lab (http://cvnlab.net) focuses on understanding the computational principles by which the brain processes visual information, and lies at the intersection of cognitive neuroscience, functional magnetic resonance imaging methods, and computational modeling. He is known for advancing high-resolution fMRI methods and for developing quantitative models that describe neural computations in the visual cortex. Dr. Kay's lab has created large-scale datasets and tools that have become widely used across the human neuroscience community.
We discuss our vision of "intensive neuroimaging" in which cognitive phenomena are extensively sampled in order to support computational modeling and detailed investigation of brain function. Importantly, intensive neuroimaging does not simply mean collecting more data --- it seeks to also maximize data quality and design experiments that enable rich hypothesis spaces. As case examples, I will discuss the large-scale 7T fMRI Natural Scenes Dataset (NSD), consisting of whole-brain BOLD responses while each of 8 participants viewed tens of thousands of natural scenes over the course of 30-40 scan sessions, as well as our ongoing Visual Cognition Dataset (VCD) which seeks to broadly sample how different tasks influence visually evoked responses in the human brain.
Seminars attendees are invited to stay for a happy hour reception after the seminar to continue the conversation with the speaker.
For additional information and upcoming events, please visit the seminar series page
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