Vedant Puri
PHD Candidate
Pittsburgh, Pennsylvania, United States
Summary
👤
Senior🎓
Top SchoolVedant Puri is a PhD candidate at Carnegie Mellon with eight years of experience building differentiable, high-order PDE solvers and scalable transformer architectures tailored to CFD and battery simulations. He bridges academic rigor and production engineering—contributing test infrastructure to high-profile SciML projects like OrdinaryDiffEq.jl and helping deploy neural PDEs in commercial products such as JuliaSIM. His background spans supercomputer-scale fluid dynamics, automated meshing, and deployable neural PDE architectures, reflecting deep expertise in numerical methods and scientific machine learning. Based in Pittsburgh, he combines hands-on solver development in Julia with attention to testability and integration, and he often works at the intersection of geometry, meshing, and differentiable simulation.
8 years of coding experience
1 year of employment as a software developer
Delhi Public School Mathura RoadUniversity of Illinois Urbana-ChampaignDoctor of Philosophy - PhD, Mechanical Engineering, Doctor of Philosophy - PhD, Mechanical Engineering at Carnegie Mellon University
English, Hindi
Github Skills (7)
ode10
scim10
ordinary-differential-equations10
scientific-machine-learning10
julia10
test-automation10
testing10
Programming languages (9)
Github contributions (5)
SciML/OrdinaryDiffEq.jl
Apr 2022 - Sep 2022
High performance ordinary differential equation (ODE) and differential-algebraic equation (DAE) solvers, including neural ordinary differential equations (neural ODEs) and scientific machine learning (SciML)
Role in this project:
QA Engineer / Test Automation Engineer
QA Engineer / Test Automation EngineerContributions:22 reviews, 22 commits, 16 PRs in 5 months
Contributions summary:Vedant primarily contributed to the testing infrastructure of the OrdinaryDiffEq.jl repository. Their commits focused on creating and modifying test files, specifically targeting the `interface` directory. The commits included adding tests for linear solver integration within split ODE problems and debugging failing tests. The user also worked on test configurations to validate the correctness of the solvers and the integration with external libraries, such as LinearSolve.
adaptiveodesscientific-machine-learningdifferential-algebraicdifferential
vpuri3/NeuralPDE.jl
Aug 2021 - Apr 2022
Physics-Informed Neural Networks (PINN) and Deep BSDE Solvers of Differential Equations for Scientific Machine Learning (SciML) accelerated simulation
Contributions:11 commits, 10 pushes, 3 branches in 8 months
simulationscientific-machine-learningdifferentialodeordinary-differential-equations
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