Hariprashad Ravikumar

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 a full-stack enterprise physics 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
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0+senior R&D engineers and subject-matter experts in daily production use across WD's US and Japan sputtering/media 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)

Experience timeline

Western Digital · San Jose, CA · Summer 2026

Modeling & Simulation Intern

I built a full-stack enterprise HAMR simulator now used daily by 40+ senior R&D engineers and subject-matter experts 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 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+

R&D experts

2

R&D projects shipped

1

hackathon award

PythonDashPlotlyKubernetesJenkinsMonte CarloHAMR Physics

Featured projects

All projects →
Screen recording of the Calibrated Cost-Aware LLM Router live demo: a prompt routed to a model tier in real time, with the architecture diagram and cost-savings meter updating live

Cost-Aware Multi-Agent LLM Router

Calibrated router that cuts LLM API cost 93.9% at accuracy parity (96.0% vs 92.0%, overlapping CIs) versus always calling the most capable model.

FastAPIscikit-learnPostgreSQLRedisDocker
Demonstrating the HAMR DCSNR simulator to engineers
Enterprise Physics SimulatorHAMR 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 daily by 40+ senior R&D engineers and subject-matter experts across WD's US and Japan sites.

PythonDashPlotlyKubernetesJenkins

AI-DataScience-Lab

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

FlaskAzureReactscikit-learn

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

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

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.