Integrated Sciences student at Claremont McKenna College. I build GPU physics simulations, and the checks that make their results trustworthy.

Summer 2026: NSF SCIPE REU scholar (Chishiki AI scholarship), GeoElements Lab, UT Austin, with simulation work on TACC's Vista (NVIDIA GH200) and Lonestar6 (NVIDIA A100) systems. PI: Dr. Krishna Kumar.

Water coloured by speed around a 1,100 kg Toyota Yaris hull, run g64_m1100 from the 17-run sweep.

Can a specific car cross a specific flooded road? I compared three answers of increasing cost: a depth rule of thumb, the published Australian Rainfall and Runoff (AR&R) vehicle hazard criterion, and a coupled material point method (MPM) simulation of water and a rigid vehicle hull on GPUs.

• 17 simulation runs with a provenance record that says how each field was obtained. Each run logged its own grid and physics settings. The code commit, solver version and mesh hash were filled in afterwards and are labelled that way: the commit is a reconstruction, not a record of what ran.
• Caught a rule being applied halfway. The depth x velocity product on its own is only part of the published AR&R rule, and an earlier version of this project's own code used it that way. Applying the full two-part rule for the car's class moved 23 of 70 flood scenarios to NO-FORD, and none the other way.
• 3D scene reconstruction. Trained a 1,147,694-Gaussian splat of a real drainage crossing with gsplat (30,000 iterations, PSNR 22.74).
• Open results. An interactive demo, published datasets with full data cards, and automated checks that run in GitHub Actions.

Code · Live demo · Findings · Scenario data · Load-surface data

I also contributed watertight-mesh particle seeding and content-based PLY loading to a fork of the lab's Warp-based MPM engine: jcerrell-IS/mpm-engine.

Python · NumPy · matplotlib · NVIDIA Warp (warpmpm) · gsplat · Slurm on TACC · Linux · Git and GitHub Actions · Gradio · Hugging Face Hub · Weights & Biases

LinkedIn · Hugging Face · jcerrell29@students.claremontmckenna.edu