Biography
I am working on experimental particle physics using proton-proton collision data from the Large Hadron Collider as a member in the UW Elementary Particle Experiment Group. My research interests range from dark matter searches with the ATLAS experiment, neutrino cross-section measurement with the FASER experiment, and innovative Artificial Intelligence algorithms for data-intensive discovery. My team collaborates with Prof. Scott Hauck’s ACME Lab to work on the ATLAS Pixel operation, the ATLAS Inner Tracker Pixel upgrade, and accelerated machine learning with heterogeneous computing. In addition, I led physics outreach events to promote STEM education.
Download my Curriculum Vitae.
Interests
- High Energy Physics
- Artificial Intelligence
- Heterogeneous computing
Education
-
PhD in Particle Physics, 2003-2008
University of California San Diego
-
MS in Particle Physics, 1999-2000
National Taiwan University
-
BSc in Physics, 1995-1999
National Taiwan Unvieristy
Experience
Professor
University of Washington
Director
NSF HDR Institute A3D3
Associate Professor
University of Washington
Assistant Professor
University of Washington
Postdoc
Lawrence Berkeley Natonal Lab
Awards
Undergraduate Research Mentor award
DOE Early Career Award
Own Chamberlain Fellowship
Projects
The FASER Experiment
The ATLAS Experiment
Featured Publications
Permutationless Many-Jet Event Reconstruction with Symmetry Preserving Attention Networks
We present a novel approach to this class of problem, based on neural networks using a generalized attention mechanism, that we call Symmetry Preserving Attention Networks (SPA-Net) to outperform existing state-of-the-art methods for jet-parton assignment.
FPGAs-as-a-Service Toolkit (FaaST)
Heterogeneous computing has the potential for significant gains over traditional computing models. This work represents the first open-source FPGAs-as-a-service toolkit.
GPU coprocessors as a service for deep learning inference in high energy physics
Graphics Processing Units can accelerate algorithms to resolve tremendously growing demands for computing in large scientific experiments. We present a comprehensive exploration of the use of GPU-based hardware acceleration for deep learning inference within the data reconstruction workflow of high energy physics.
Recent Publications
Contact
Sponsors
|
|
|---|