Aayush Sabharwal
Member Of Steering Council at SciML
Gurugram, Haryana, India
Summary
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Rockstar🎓
Top SchoolAayush Sabharwal is a software engineer at JuliaHub and a Member of the SciML Steering Council with eight years of experience building open-source scientific tooling. He is the lead developer of ModelingToolkit.jl, one of the most widely used Julia packages and the second most-starred project in the SciML ecosystem, enabling automated transformations and symbolic-numeric workflows for differential equations and optimization. His work blends computer algebra, high-performance back-end engineering, and user-facing documentation to make complex mathematical modeling accessible to researchers. Aayush has contributed notable examples and visualization improvements across JuliaDynamics and Agents.jl, showing a taste for both algorithmic rigor and practical demos. An IIT Ropar CSE graduate, he has also mentored student communities and participated in GSoC, reflecting a sustained commitment to growing the Julia ecosystem. He combines deep domain knowledge in scientific machine learning with a knack for turning symbolic math into reliable, parallelizable code.
8 years of coding experience
Bachelor's degree Computer Science, Bachelor's degree Computer Science at Indian Institute of Technology, RoparDelhi Public School Vasant Kunj
Stackoverflow
11reputation
2kreached
2answers
0questions
Github Skills (16)
symbolic-computation10
agent-based-modeling10
differential-equations10
scientific-machine-learning10
julia10
optimization9
computer-algebra9
dao8
algorithms8
data-structures8
algorithm8
data-structure8
ode8
ios6
unity-game-engine6
Programming languages (8)
Github contributions (5)
JuliaDynamics/Agents.jl
Jan 2021 - Oct 2022
Agent-based modeling framework in Julia
Role in this project:
Back-end Developer
Back-end DeveloperContributions:55 reviews, 69 commits, 44 PRs in 1 year 9 months
Contributions summary:Aayush primarily worked on implementing and improving a diffusion-limited aggregation example within the Agents.jl framework. Their contributions include initial implementation, updates to improve particle behavior, and refactoring code. They also enhanced the visualization of the example and integrated features such as varying particle size and speed calculations. Finally, the user documented the fractal growth example.
simulation-frameworksimulationmodeling-toolcrosswordindividual-based-modelling
SciML/ModelingToolkit.jl
Aug 2021 - Dec 2022
An acausal modeling framework for automatically parallelized scientific machine learning (SciML) in Julia. A computer algebra system for integrated symbolics for physics-informed machine learning and automated transformations of differential equations
Role in this project:Back-end Developer
Back-end DeveloperContributions:183 reviews, 23 commits, 325 PRs in 1 year 3 months
Contributions summary:Aayush primarily contributed to the `modelingtoolkit.jl` repository by modifying core system components to utilize new features from `DiffEqFunctions`. They updated several system files, including those related to optimization, nonlinear systems, and differential equations, to incorporate parameters and independent variables. Further contributions included adding tutorial documentation and updating the codebase to align with changes in the underlying library.
sdescientific-machine-learningtransformationspartial-differential-equationsdifferential
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