Utkarsh R
Graduate Research Assistant
Cambridge, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolUtkarsh R is a computational scientist and software engineer with eight years of experience accelerating differential-equation simulations via parallel computing and scientific machine learning, currently a graduate research assistant at MIT CSAIL’s Julia Lab. He contributes to high-performance SciML tooling—most notably implementing advanced Runge-Kutta methods and adaptive error estimation in the widely used OrdinaryDiffEq.jl library—bringing numerical analysis rigor to production-grade solvers. His background spans internships and consulting at Julia Computing, AWS, NVIDIA, and industry research teams, where he translated research into scalable simulation and inference systems. With dual BTech degrees from IIT Kanpur and ongoing PhD work at MIT, he blends deep theoretical training with hands-on optimization across languages and cloud platforms. An unexpected through-line in his career is consistent focus on reducing memory allocations and numerical dissipation, reflecting a pragmatic obsession with both accuracy and performance.
8 years of coding experience
96.6% (Valedictorian), 96.6% (Valedictorian) at Cambridge School, IndirapuramDoctor of Philosophy - PhD, Computational Science and Engineering, Doctor of Philosophy - PhD, Computational Science and Engineering at Massachusetts Institute of TechnologyIndian Institute of Technology Kanpur
Github Skills (6)
differential-equations10
ode10
numerical-methods10
performance-optimization10
scientific-computing10
julia10
Programming languages (10)
Github contributions (5)
SciML/OrdinaryDiffEq.jl
Feb 2020 - Mar 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:
Back-end Developer
Back-end DeveloperContributions:4 reviews, 230 commits, 60 PRs in 2 years 1 month
Contributions summary:Utkarsh contributed to the implementation of Runge-Kutta methods, specifically adding a FRK65 Butcher Table and a basic performstep implementation. They further developed and tested adaptive error estimation (EEST) for these Runge-Kutta methods, including the addition of zero-dissipation fitted coefficients. The user also implemented and tested a PFRK87 Butcher Table, along with its perform step function.
adaptiveodesscientific-machine-learningdifferential-algebraicdifferential
utkarsh530/ESO208A
Jan 2019 - Aug 2019
Contributions:2 commits, 7 pushes, 1 branch in 6 months
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