eventWorkshop
Tim de Silva | AI Financial Advice: Supply, Demand, and Life Cycle Implications
SeminarOct 07, 202612:00 PM - 1:15 PM
October
07
2026
Get the latest news, advances in research, policy work, and education program updates from HAI in your inbox weekly.
Sign Up For Latest News
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: Discover how to use CuPyNumeric for seamless scaling numPy code to multi-GPU and multi-node setups. This workshop will discuss the best practices and uses of cuPyNumeric with real world examples and performance comparisons.
Speaker: Marcus Manos is a Solutions Architect at NVIDIA working with Financial Services customers. He received his masters in Data Science from the University of California, Berkeley; where he trained deep learning models. While at Nvidia, Marcus has worked on a diverse set of problems involving LLM inference, reasoning model training, cuPyNumeric.
Speaker: Manolis Papadakis is a Software Engineering Manager at NVIDIA. Manolis joined NVIDIA after completing his Ph.D. in computer science at Stanford University, with a focus on distributed and scientific computing. He has since been working on making Legate/cuPyNumeric a reality.
We are excited to announce a series of NVIDIA-led workshops designed to enhance your expertise in GPU computing. Whether you're a beginner or looking to deepen your skills, these sessions offer valuable insights and hands-on learning opportunities. Workshops are expected to be held on a monthly basis.
