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.
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
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.
Heterogeneous computing has the potential for significant gains over traditional computing models. This work represents the first open-source FPGAs-as-a-service toolkit.
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.