Research
I work at the intersection of quantum field theory and large-scale computation — using GPU-accelerated HPC and machine learning to extract physics from simulations that produce terabytes of noisy data.
I'm a theoretical particle physics PhD candidate at New Mexico State University, USA, specializing in the application of GPU-accelerated high-performance computing (HPC) and machine learning to fundamental physics problems. My work focuses on studying the intrinsic motion of quarks and gluons and exploring Beyond Standard Model (BSM) physics through large-scale simulation quantum field theory and symmetries.
My PhD research under Dr. Michael Engelhardt focuses on lattice quantum chromodynamics (QCD) calculations of Transverse Momentum Dependent Parton Distribution Functions (TMDs). To achieve this, I built an end-to-end machine learning pipeline to process over 30,000+ multidimensional observables from Monte Carlo simulations, achieving 93%+ predictive accuracy using symbolic regression (PySR). To handle the multi-terabyte datasets, I developed GPU-accelerated CUDA C++ pipelines that reduced data processing time by 10× on HPC clusters, alongside production-grade packages to manage resampling and ensure numerical stability.
Recently, as a Modeling and Simulation Intern at Western Digital, I applied my computational physics and software engineering expertise to next-generation Heat-Assisted Magnetic Recording (HAMR) technology. I developed a closed-form analytical model from first principles to evaluate write behavior and predict adjacent track erasure (ATI & xTI), with a peer-reviewed publication currently in progress. Simultaneously, I built and deployed “NIMBLE”—a full-stack interactive simulation web application using Python, Dash, and Kubernetes. Packaged as a modular Python library, this simulator is now actively used by cross-functional engineering teams to evaluate recording performance and accelerate hardware development cycles.
In addition to my core research and industry work, I maintain active independent collaborations. With Los Alamos National Laboratory, I develop and optimize parallelized C++ CUDA kernels on HPC clusters (NERSC Perlmutter) to accelerate multi-terabyte calculations for nucleon Electric Dipole Moments (EDMs). Concurrently, I collaborate with North Carolina State University, utilizing Mathematica symbolic computation workflows on HPC clusters to analyze complex algebraic structures and relativistic symmetry constraints.
As I anticipate defending my dissertation in December 2026, my background provides a unique blend of deep physics intuition and hands-on expertise in C++/CUDA, parallel computing, and machine learning. I am driven to apply these skills to solve complex, data-intensive challenges and contribute to cutting-edge scientific and technical advancements in the industry.
Lattice QCD & Transverse Momentum Dependent Distributions
PhD, Dr. Michael Engelhardt, NMSU
How do quarks and gluons move inside the proton? End-to-end ML pipeline over 30,000+ observables from Monte Carlo simulation, 93%+ accuracy via symbolic regression (PySR); GPU-accelerated CUDA C++ reduced processing time 10×; jackknife/bootstrap uncertainty quantification.
Nucleon Electric Dipole Moments
Collaboration with Los Alamos National Laboratory
Parallelized C++/CUDA kernels on NERSC Perlmutter; 75,000+ via custom SLURM workflows.
Conformal Algebra Interpolation
Collaboration with Prof. Chueng-Ryong Ji, NC State
Mathematica symbolic-computation workflows analyzing algebraic structures and relativistic symmetry constraints. Published in Physical Review D 113, 096018 (2026); (3+1)-dimensional extension in preparation.