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: As foundation models grow in scale and complexity, the need for reproducible, modular, and high-performance research frameworks has become critical. NVIDIA NeMo is an open, extensible framework built on PyTorch and tightly integrated with Megatron-Core and Transformer Engine for optimized distributed training on NVIDIA GPUs, providing a unified stack for developing end-to-end generative AI models. This talk will introduce key components of the NeMo ecosystem, including data curation, model customization through techniques like parameter-efficient fine-tuning (PEFT) and model alignment algorithms, model safety through guardrails, as well as 4D parallelism for efficient large-model training. Drawing on examples from robotics research, the presentation will highlight how NeMo can be used to build and adapt foundation models for multimodal and domain-specific reasoning, illustrating how NeMo’s modular design enables scalable, domain-specific generative AI for complex systems.
Speaker: Sugandha Sharma is a senior generative AI architect and scientist at NVIDIA, specializing in generative models for robotics and embodied AI. Prior to joining NVIDIA, she was a research scientist at Microsoft Research, where she worked on GPT-based gaming AI agents and their alignment with humans. She holds a PhD in theoretical neuroscience from MIT, where she developed generative AI agents for 3D spatial planning, a memory model without catastrophic forgetting, and the first neural circuit model linking memory and spatial navigation, significantly advancing the theoretical understanding of brain circuits.
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.
