Vaibhav Dixit
Business Engineer at Meta
Palo Alto, California, United States
Summary
🤩
Rockstar🎓
Top SchoolVaibhav Dixit is a Business Engineer and research-driven developer with nine years of experience building scientific machine learning tools and production software, most recently at Meta and previously as a core developer at SciML and Julia Computing. He specializes in numerical optimization, neural differential equations, and scalable SciML frameworks—having been the primary developer on Optimization.jl and contributed key features to ModelingToolkit.jl and DiffEqFlux.jl used by the wider Julia scientific community. His work spans product-focused healthcare and pharma tooling (precision dosing, NLME/QsP) and foundational library engineering, combining rigorous testing, AD backend integration, and algorithmic refactors. Based in Palo Alto and an MIT CSE master’s student, he pairs academic depth with practical delivery across industry and open source, and has a track record of shipping 0-to-1 healthcare-focused solutions and winning LLM hackathon projects.
9 years of coding experience
7 years of employment as a software developer
Master of Science - MS Computational Science and Engineering, Master of Science - MS Computational Science and Engineering at Massachusetts Institute of TechnologyHigh School, High School at Delhi Public School, BhilaiIntegrated Dual Degree (B-Tech & M-Tech) Mathematics and Computing, Integrated Dual Degree (B-Tech & M-Tech) Mathematics and Computing at Indian Institute of Technology (Banaras Hindu University), Varanasi
Github Skills (17)
symbolic-computation10
computer-algebra10
ziggy10
ode10
testing10
differential-equations10
zig10
neural10
flux10
fluxor10
scientific-machine-learning10
julia10
optimization10
convex-optimization9
nonlinear-optimization9
Programming languages (13)
Github contributions (5)
SciML/Optimization.jl
May 2020 - Jan 2023
Mathematical Optimization in Julia. Local, global, gradient-based and derivative-free. Linear, Quadratic, Convex, Mixed-Integer, and Nonlinear Optimization in one simple, fast, and differentiable interface.
Role in this project:
Back-end Developer & QA Engineer
Back-end Developer & QA EngineerContributions:226 reviews, 272 commits, 452 PRs in 2 years 9 months
Contributions summary:Vaibhav primarily contributed to the testing and development of the GalacticOptim.jl package. Their work involved creating and modifying test files to ensure the library functions correctly. The user added and updated tests related to constraints, minibatching, and AD backends. Furthermore, the user made code modifications related to gradient calls and update! dispatch, and the use of `data` argument, indicating debugging and enhancement work.
optimization-methodsnonlinear-optimizationautomatic-differentiationscientific-machine-learningalgorithmic-differentiation
SciML/ModelingToolkit.jl
Mar 2018 - Jan 2023
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:42 reviews, 18 commits, 22 PRs in 4 years 11 months
Contributions summary:Vaibhav contributed significantly to the `modelingtoolkit.jl` repository, focusing on enhancing the framework's capabilities for scientific machine learning. Their work included the addition of core features like `JumpVariable` and `NoiseVariable`, implementing the chain rule for derivative calculations, and integrating new functionalities such as constraint handling. They also refactored the code, removing legacy optimization functions in favor of modern solutions.
sdescientific-machine-learningtransformationspartial-differential-equationsdifferential
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