Guest Speaker: Shih-Chieh Hsu, Associate Professor, Department of Physics,
University of Washington
This will be a HYBRID event. Please indicate on your registration if you will attend virtually or in-person.
- In-Person Location: Northwest Corner Building, 14th Floor (DSI Suite) – 550 W 120th St, New York, NY 10027
- Virtual: Zoom link to be sent upon registration
Accelerating Artificial Intelligence for Data-Driven Discovery
Abstract: As scientific data sets become progressively larger, algorithms to process this data quickly become more complex. In response, Artificial Intelligence (AI) has emerged as a solution to efficiently analyze these massive data sets. Emerging processor technologies such as graphics processing units (GPUs) and field-programmable gate arrays (FPGAs) allow AI algorithms to be greatly accelerated. The Accelerated AI Algorithms for Data-Driven Discovery (A3D3) Institute, sponsored by the National Science Foundation, under the Harnessing the Data Revolution program, is established to enable real-time AI at scale for broad applications. In this talk, I will give an overview about the challenges of high energy physics, multi-messenger astrophysics and neuroscience regarding AI across latency and throughput regimes. I will introduce various techniques for model compression using state-of-the-art techniques such as pruning and quantization for edge computing. I will demonstrate that that acceleration of AI inference as a web service represents a heterogeneous computing solution. Finally, I’ll discuss how A3D3 can bring together disparate communities that are threaded by common data-intensive grand challenges to accelerate discovery in Science and Engineering.
Short Bio: Shih-Chieh Hsu earned a MS degree in Physics from National Taiwan University and a PhD in Physics from University of California San Diego. He is currently an Associate Professor in Physics and Adjunct Associate Professor in Electrical and Computer Engineering at University of Washington (UW), and Director of NSF HDR Institute: Accelerated Artificial Intelligence Algorithms for Data-Driven Discovery (A3D3). He is working on experimental particle physics using proton-proton collision data from the Large Hadron Collider. His research interests range from dark matter searches with the ATLAS experiment, neutrino cross-section measurement with the FASER experiment, innovative Artificial Intelligence algorithms for data-intensive discovery and accelerated machine learning with heterogeneous computing. He is a recipient of DOE Early career award and UW Undergraduate research mentor award.
Data Science Institute, Columbia University
About the Organizers
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