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) & 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 & neuroscience regarding AI across latency & throughput regimes. I will introduce various techniques for model compression using state-of-the-art techniques such as pruning & 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 & Engineering.
Short Bio: Shih-Chieh Hsu earned a MS degree in Physics from National Taiwan University & a PhD in Physics from University of California San Diego. He is currently an Associate Professor in Physics & Adjunct Associate Professor in Electrical & Computer Engineering at University of Washington (UW), & 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 & accelerated machine learning with heterogeneous computing. He is a recipient of DOE Early career award & UW Undergraduate research mentor award.