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This seminar pairs a case study of AI diffusion with an organizational approach to technological changes in the workplace.
This seminar pairs a case study of AI diffusion with an organizational approach to technological changes in the workplace.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Sessions run Wednesdays from 4:30–5:30 PM in CoDa E160. Each session features a different Stanford speaker; talk titles are announced by the organizers.
Abstract: This talk will cover how Graph Neural Networks can be used to enhance LLMs using PyG to improve accuracy for RAG like tasks across any kind of data domain. This will include examples on real world data. We will also cover how LLMs can be used to enhance GNNs for graph machine learning tasks. While not running on Marlowe, the techniques can be applied to any GPU cluster!
Speaker: Rishi Puri graduated from UC Berkeley and is a lead engineer for the Deep Learning FrameWork PyG at NVIDIA. He is also a core contributor to the open source PyG framework and community. His main focus is researching how to combine state of the art graph and language modeling techniques. He enjoys teaching about this work at Stanford, conferences, webinars, and through the PyG Slack and LinkedIn communities.
We are excited to announce a series of Marlowe 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.
