Hariprashad Ravikumar

Computational physicist building GPU-accelerated ML and simulation tools.

Graduating December 2026 · Open to Research Scientist / ML Engineer roles · SF Bay Area

I turn large-scale physics simulations into software that engineers actually use. At Western Digital I derived closed-form models for heat-assisted magnetic recording from first principles and shipped HAMR DCSNR NIMBLE app, the simulator that the team now runs across sites in the US and Japan.

My PhD at New Mexico State University applies GPU-accelerated HPC and machine learning to lattice QCD with 30,000+ observables, CUDA C++ pipelines, and symbolic regression that recovers analytical structure from noisy Monte Carlo data using HPC (High Performance Computing).

Portrait of Hariprashad Ravikumar
0+active users across WD USA and Japan; adopted by sputtering and media engineering teams
0%predictive accuracy with symbolic regression machine learning
0+CPU/GPU hours on NERSC Perlmutter
0+multiterabit observables processed in HPC (C++, Lua, SLURM)

Western Digital · San Jose, CA · Summer 2026

Modeling & Simulation Intern

I built NIMBLE, a HAMR simulator now used by 40+ engineers across WD's US and Japan sites.

Over summer 2026 I derived closed-form analytical models for grain magnetization dynamics in Heat-Assisted Magnetic Recording from first principles, predicting adjacent-track erasure (ATI & xTI) without costly hardware experiments.

I shipped the physics as NIMBLE, a production Dash/Plotly simulation platform packaged as a modular Python library, deployed on Kubernetes with Jenkins CI/CD and adopted by sputtering and media engineering teams.

40+

engineers

2

R&D projects shipped

1

hackathon award

PythonDashPlotlyKubernetesJenkinsMonte CarloHAMR Physics

Featured projects

All projects →
Demonstrating the NIMBLE HAMR DCSNR simulator to engineers
NIMBLEHAMR DCSNR Simulator · Western Digital
PythonDashPlotly

Intern – Media Test Engineering (HAMR Modeling & Simulation) @ Western Digital, San Jose, CA, USA

Production simulation platform for heat-assisted magnetic recording, used by 40+ engineers across WD's US and Japan sites.

PythonDashPlotlyKubernetesJenkins
Lattice QCD TMD pipeline cover graphic

Lattice QCD TMD Pipeline

GPU-accelerated ML pipeline extracting analytical structure from 30,000+ lattice QCD observables.

CUDA C++PySRSLURMPython

AI-DataScience-Lab

Full-stack forecasting app: CSV upload, pandas cleaning, scikit-learn regression, GPT-3.5 summaries.

FlaskAzureReactscikit-learn

Experience timeline

Tech stack

Languages

PythonC++CUDALuaBashJavaScript/TypeScriptLaTeX

ML & Scientific

PyTorchTensorFlowScikit-learnSciPyNumPypandasPySRPhysics-Informed ML

HPC & Parallel

MPIOpenMPSLURMMulti-GPUcuFFTNERSC Perlmutter

Web & Visualization

DashPlotlyFlaskReactMatplotlibThree.js

DevOps

DockerKubernetesJenkinsGitHub ActionsCI/CDGitAzureAWS

Peer-reviewed · Physical Review D

Interpolating conformal algebra in (1+1) dimensions between the instant form and the light-front form of relativistic dynamics.

Ji, C.-R. & Ravikumar, H. (2026). | Physical Review D 113, 096018. American Physical Society.

Physical Review D, American Physical SocietyAmerican Physical Society logo

Currently interviewing for full-time roles.

Graduating December 2026. Looking for Research Scientist, Applied/ML Scientist, HPC & Scientific Computing Engineer, or Software Engineer roles in the SF Bay Area. Authorized to work in the U.S. under STEM OPT; open to H-1B sponsorship.