Date & Time:
May 6, 2024 2:30 pm – 3:30 pm
Location:
JCL 390
05/06/2024 02:30 PM 05/06/2024 03:30 PM America/Chicago Shuaiwen Leon Song (Together AI) – DeepSpeed4Science: Enabling System Support for Large Signature AI4Science Models at Scale JCL 390

Abstract:  With the new era of AIGC and large-scale language models being applied to change the landscape of the scientific discovery, I led the DeepSpeed@Microsoft to establish a new initiative with our partners from industry, academia and federal research labs to enable new science-driven system technologies to support large-scale science discovery (https://www.microsoft.com/en-us/research/blog/announcing-the-deepspeed4science-initiative-enabling-large-scale-scientific-discovery-through-sophisticated-ai-system-technologies/). In this talk, I will cover several of our signature models and engagements from our first releases of DeepSpeed4Science and discuss what system technologies have been developed and some future endeavors. Finally, I will show my vision on how to build a ML system research capability and community (e.g., a large collaborative initative) for scientific discovery at University of Chicago, serving as a public forum for scientists around the world to quickly acquire the essential system technologies that bottleneck their model development/deployment and contribute to this roadmap.

Speakers

Shuaiwen Leon Song

Vice President of Research and Frontier Technologies, Together AI

Shuaiwen Leon Song is the vice president of research and frontier technologies at Together AI, a unicorn AI startup in San Francisco focusing on building the fastest and most cost-efficient large-scale cloud services for the generative AI era. Prior to this role, he was a senior principal scientist and manager at Microsoft. He was also the chief scientist for Deepspeed4Science initiative which created a broad engagement between Microsoft, Microsoft research, DoE labs, academia and industry partners to enable sophisticated system technology research and development for supporting various aspects of training and inference for large-scale AI-driven scientific models. At Microsoft, he also managed the Brainwave for Bing, a legacy AI infrastructure project for cloud inference. Prior to Microsoft, he was the SOAR associate professor at University of Sydney and an adjunct professor at University of Washington. His past works in HPC have received several best paper nominations and were featured in U.S. DoE research highlights and other media outlets. He was the recipient of several awards including IEEE early-career award for HPC, IEEE mid-career award for scalable computing, Facebook faculty award, Google brain faculty award, Australian most innovative engineer award, and AIR global faculty award.

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