eventWorkshop
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.
This session explores how to efficiently run and monitor large workloads in hyper-scalars and the kind of telemetry data we can monitor from the Infra and workload side. We will review various tools offered by NVIDIA to monitor the performance of a workload and how they improve stability with increasing scale.
Aparnaa Ramani is an AI Solution Architect working closely with leading AI labs. She collaborates closely with them on optimizing workloads and data center bring-ups with the latest NVIDIA architecture. Before joining NVIDIA, she worked as a GPU kernel developer at Qualcomm. Aparnaa has a Master's degree from Georgia Tech with focus on statistical ML and Advanced DSP.
