Xingjian Guo
Software Engineer at Oasa
Richmond, Texas, United States
Summary
👤
Senior🎓
Top SchoolXingjian Guo is a software engineer with 8 years of experience building machine learning and smart-device applications, specializing in embedded systems, robotics simulation, and automated testing pipelines. He has shipped voiceAI and special effects SDKs at SoundHound and ByteDance and now accelerates robotics R&D and field testing from Houston-area deployments. A contributor to high-performance scientific computing—having improved numerical integrators and added Krylov support to the widely used OrdinaryDiffEq.jl—he brings strong numerical methods expertise alongside product-focused engineering. With an MS in Scientific Computing and a physics background from Peking University, he blends rigorous simulation skills with practical embedded systems delivery.
8 years of coding experience
5 years of employment as a software developer
Master of Science - MS Scientific Computing, Master of Science - MS Scientific Computing at New York UniversityMinor Mathematics, Minor Mathematics at Peking University
Github Skills (4)
differential-equations10
complex-numbers10
numerical-integration10
julia10
Programming languages (4)
Github contributions (5)
SciML/OrdinaryDiffEq.jl
Feb 2018 - Nov 2018
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 & Numerical Solver Engineer
Back-end Developer & Numerical Solver EngineerContributions:1 release, 244 commits, 40 PRs in 8 months
Contributions summary:Xingjian primarily contributed to enhancing the performance and functionality of the ordinarydiffeq.jl library, focusing on numerical integration techniques. They fixed bugs related to tolerance values in the integrator, and made the `Trapezoid` method and other SDIRK integrators compatible with complex data. Furthermore, the user updated the test scripts to reflect the improvements and added support for Krylov methods, enhancing the overall robustness and capabilities of the solver.
adaptiveodesscientific-machine-learningdifferential-algebraicdifferential
MSeeker1340/OrdinaryDiffEq.jl
Feb 2018 - Aug 2018
DiffEq solvers for ordinary differential equations
Contributions:86 pushes, 47 branches in 5 months
equationsdifferentialequationsordinary-differential-equationsddesolvers
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