Curriculum Vitae

Education

PhD in Physics
September 2023 - present
University of Washington
Seattle, WA
Advisors: Quentin Buat, Xiangyang Ju (LBNL)
  • Courses taken: Deep Learning, Computer Systems, Quantum Field Theory, Theory of Solids
  • Candidacy achieved December 2025
  • Research interests: Physics analysis, GPU acceleration, scientific machine learning
Master of Science in Physics
September 2023 - June 2025
University of Washington
Seattle, WA
  • Select courses taken: Quantum Mechanics, Electricity and Magnetism, Statistical Physics, Mechanics
Bachelor of Arts in Physics, Summa Cum Laude
September 2019 - June 2023
Lawrence University
Appleton, WI
Advisors: Megan Pickett, Alexander Heaton
  • Independent research in scientific machine learning and physics-informed neural networks
  • Developed physics-informed neural network to solve Einstein's field equations to numerically obtain the Schwarzschild metric

Technical Skills

Languages
C++, Python, Julia, Bash
Machine learning
PyTorch, ONNX, NVIDIA Triton Inference Server, multi-node distributed training on HPC
Infrastructure
Kubernetes, Docker, Dask, GPU throughput/latency profiling, Prometheus & Grafana monitoring
Scientific tools
ROOT, ATLAS Athena, NumPy/SciPy, statistical fitting and systematics, Git/GitLab CI

Research Experience

Development of Transformer-based tau reconstruction AI model
Jan. 2026 - present
University of Washington and Lawrence Berkeley National Lab
Seattle, WA and Berkeley, CA
  • Developed and tested novel model architecture for tau reconstruction.
  • Implemented custom plotting analysis suite for benchmarking and performance evaluation.
Deployment of GPU tracking as-a-Service for the ATLAS detector
Jun. 2024 - present
University of Washington and Lawrence Berkeley National Lab
Seattle, WA and Berkeley, CA
  • Implemented custom C++ backend to run inference at scale for online data-taking.
  • Developed deployment system on Kubernetes clusters for data-taking systems.
  • Extensively tested throughput and latency of pipeline through custom metrics and dashboards.
Analysis of CP violation in Z → ττ events
Sep. 2023 - present
University of Washington and CERN
Seattle, WA and Geneva, CH
  • Novel analysis of di-tau final states using ATLAS data.
  • Developed understanding of the novel observable, analysis framework, and statistical fitting framework.
Analysis Grand-Challenge Developer
Jun. 2026 - present
University of Washington
Seattle, WA
  • Developed and tested DASK-based tools to accelerate particle-physics analysis.
  • Implemented inference as-a-Service infrastructure and statistical analysis tools.

Awards and Fellowships

2026

Office of Science Graduate Student Research (SCGSR) Fellowship
U.S. Department of Energy • $21,600
Analysis Fellowship
IRIS-HEP • $10,000

2025

Graduate Research Fellowship Program, Honorable Mention
National Science Foundation

2024

Western Advanced Training for Computational High-Energy Physics (WATCHEP) Fellowship
Department of Energy (DOE) • $65,000 / year

2023

Provost Award
University of Washington • $10,000
Physics Department Fellowship
University of Washington • $5,000

2022

J. Bruce Brackenridge Prize for excellence in physics
Lawrence University
Maurice Cunningham Phi Beta Kappa Prize for highest GPA in junior class
Lawrence University

2021

Sir Isaac Newton (SIN) award for creativity in computational physics problem-solving
Lawrence University
Ralph White Prize in Mathematics
Lawrence University

Outreach & Professional Development

Service and Outreach

AI Future Lab Lecturer and Lab Leader
2026
Prepared lab exercises on introductory AI coding concepts for 30 high-school students from Taiwan, and gave a lecture on AI ethics.
U.W. A3D3 Machine Learning Hackathon
2025
Organized an ML hackathon at University of Washington for more than 30 students on AI prediction tasks using scientific data.
U.S. LHC Users Association D.C. Trip
2025
High-Energy Physics funding ambassador, advocating for funding in Congress directly with House and Senate members.
Exploring the Quantum Universe with Artificial Intelligence
2024
Undergraduate Symposium Moderator and Mentor.
IMOD outreach with Rainier Prep. Middle School
2024
Introduced experimental science to 90 fifth grade students through engaging interactive activities.

Development

WATCHEP Summer School. Lawrence Berkeley National Lab, Summer 2025. This joint summer school with other computing physics groups in cosmology and particle physics focused on the breadth of physics and computing currently being explored in science and industry.
Deep Learning at Scale Training. NERSC facility at Lawrence Berkeley National Lab, 2025. This training focused on distributed training and data parallelization techniques for training models on high-performance computing systems.
Machine Learning for Fundamental Physics School. Lawrence Berkeley National Lab, Summer 2024. This workshop focused on tools to deploy machine learning models for a variety of computing needs. Most relevant topics included deployment of models on FPGAs, Differential Programming, Transformers, and Unfolding using machine learning.

Mentoring

As a PhD student I have mentored four undergraduates in research techniques by introducing the ATLAS experiment to them, as well as instilling an excitement for physics research and discovery. In this role I organize weekly meetings and check-ins, as well as field questions and provide academic guidance.

Memberships

Accelerated AI Algorithms for Data-Driven Discovery (A3D3)
Phi Beta Kappa (National Honors Society)
Sigma Pi Sigma (Physics Honors Society)