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.
Abstract: Scientific machine learning (SciML) is becoming a fundamental part of research, development, and discovery workflows for scientists across many domains such as computational fluid dynamics, materials characterization, climate and weather modeling, and computer-aided engineering. With the increase in use of AI and ML in these domains also comes an increase in the number of resources available for developing models, and a plethora of datasets to use as starting points for model training. While many efforts are segmented and tailor-made for specific use cases, projects such as PhysicsNeMo, The Well, and Proxima Fusion's ConStellaration dataset are great examples of community efforts to bridge the gap between siloed SciML research and collaborative community projects. In this tutorial, PhysicsNeMo is used to expand the scope of community models and datasets from The Well and the ConStellaration Challenge by leveraging pre-trained physics-informed machine learning models, community accessible datasets, and the robust SciML framework from PhysicsNeMo.
Bio: Carmelo Gonzales is a solutions architect at NVIDIA with a background in applied scientific AI and machine learning in HPC environments. Before joining NVIDIA, he worked on AI-enabled material discovery, AI-accelerated simulations, and built open software frameworks for the scientific research community. His current work is on enabling R&D teams to adopt and apply scientific AI/ML to problems in physics, with a focus on accelerated and scalable workflows.
