Kanav Gupta
College Park, Maryland, United States
Summary
🤩
Rockstar🎓
Top SchoolKanav Gupta is a PhD candidate in computer science at the University of Maryland with eight years of engineering and research experience spanning cryptography, secure MPC, and high-performance scientific computing. He has contributed to production-grade projects at Microsoft (research fellow and intern) and Google (SWE intern), publishing work on secure two-party computation and secure ML that landed in top security conferences. An active open-source contributor and mentor in the Julia ecosystem, he has optimized ODE solvers and improved documentation/deployment pipelines for prominent SciML repositories, demonstrating both algorithmic depth and DevOps pragmatism. His hands-on work on FSS backends and PRG bugs shows a rare blend of cryptographic theory and low-level implementation skill. Based in College Park, MD, he also builds developer tooling (e.g., Bake Cloud) and has a track record of mentoring students in GSoC/GSoD, signaling a commitment to community and reproducible research.
8 years of coding experience
Indian Institute of Technology RoorkeeDoctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at University of Maryland
Japanese
Github Skills (24)
algorithm10
code-optimization10
caching10
algorithms10
back-end-development10
ordinary-differential-equations10
cicd10
differential-equations10
numerical-methods10
optimisation10
numerical-optimization10
numerical-analysis10
documentation10
optimization10
julia10
Programming languages (19)
Github contributions (5)
SciML/DiffEqDocs.jl
Jan 2019 - Aug 2020
Documentation for the DiffEq differential equations and scientific machine learning (SciML) ecosystem
Role in this project:
DevOps Engineer
DevOps EngineerContributions:23 commits, 14 PRs, 2 branches in 1 year 7 months
Contributions summary:Kanav primarily focused on modifying the documentation build and deployment process within the DiffEqDocs.jl repository. Their commits involved updating the `make.jl` file, which configures the documentation generation. Specifically, the user added netlify deploy previews and modified the deployment configuration to use a different repository. These changes aimed to improve the documentation deployment and hosting infrastructure.
equationsdaeddescimlsde
SciML/OrdinaryDiffEq.jl
Dec 2018 - Aug 2020
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 & Algorithm Optimization Engineer
Back-end Developer & Algorithm Optimization EngineerContributions:202 commits, 66 PRs, 109 pushes in 1 year 8 months
Contributions summary:Kanav's commits focus on optimizing the performance of ordinary differential equation (ODE) solvers within the SciML project. Contributions include significant code modifications, such as "Cache Reduction" and "Reuses memory of unused k" within the "verner_rk_perform_step.jl" and "verner_caches.jl" files, which directly relate to the efficient management of cache variables. Furthermore, the user worked on modifying the dense output for several methods, implying an understanding of the underlying numerical methods within the ODE solvers. The user also implemented and added testing for the Anderson acceleration method for nonlinear solvers.
adaptiveodesscientific-machine-learningdifferential-algebraicdifferential
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