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 talk covers modern post-training and reinforcement learning techniques used to train effective language models and agents. An overview of NVIDIA NeMo and Nemotron will be covered with a focus on NeMo-RL, NeMo-Gym and how these frameworks are used to train the latest Nemotron models.
Christian Munley is an applied research scientist at NVIDIA focused on post-training and alignment of language models including Nemotron. Previously, he was a solutions architect at NVIDIA, supporting partners to leverage and accelerate computing in their research and products. His education is in physics and computer science, where his diverse research topics include biophysics and LLMs for HPC.
Shibani Likhite is a Solutions Architect at NVIDIA, working closely with cloud service providers on large-scale post-training, alignment, and high-throughput inference using NVIDIA technologies. She has worked across multiple industries to enable partners in building and deploying efficient post-training and inference workflows. Prior to joining NVIDIA, she earned her Master’s degree in Computer Science from UC San Diego, with a focus on Artificial Intelligence.
