Shubham Sahu
Software Engineer at JLR
West Bengal, India
Summary
👤
Senior🎓
Top SchoolShubham Sahu is a software engineer with nine years of experience, currently building software at JLR after a stint at Goldman Sachs. Trained in chemical engineering at IIT Kharagpur, he brings a quantitative mindset to backend development and numerical computing. An active open-source contributor, he has improved high-performance ODE solvers in the SciML ecosystem by implementing and optimizing Adams–Bashforth–Moulton methods and adding rigorous convergence tests. His background includes leadership and service roles at IIT Kharagpur’s Technology Adventure Society, reflecting strong collaboration and mentoring abilities. Based in West Bengal, India, he combines domain rigor from engineering with practical software craftsmanship, often focusing on performance-critical code paths. Notably, his work on variable-step integrators demonstrates both numerical insight and a habit of validating correctness under real-world constraints.
9 years of coding experience
1 year of employment as a software developer
Bachelor's degree Chemical Engineering, Bachelor's degree Chemical Engineering at Indian Institute of Technology, Kharagpur
Github Skills (8)
differential-equations10
ode10
numerical-methods10
ordinary-differential-equations10
julia10
scientific-machine-learning9
scim9
numerical-analysis9
Programming languages (9)
Github contributions (5)
SciML/OrdinaryDiffEq.jl
Mar 2018 - Oct 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
Back-end DeveloperContributions:119 commits, 24 PRs, 149 comments in 7 months
Contributions summary:Shubham's commits focus on implementing and optimizing Adams-Bashforth-Moulton (ABM) and related numerical methods for solving ordinary differential equations (ODEs) within the SciML ecosystem. Their contributions include the addition of new methods like AB3, AB4, AB5, and various variable-step-size methods. They refactored and improved existing implementations, optimized function evaluations to enhance performance, and added convergence tests to validate the correctness and efficiency of the new methods.
adaptiveodesscientific-machine-learningdifferential-algebraicdifferential
sipah00/OrdinaryDiffEq.jl
Mar 2018 - Oct 2018
DiffEq solvers for ordinary differential equations
Contributions:4 PRs, 98 pushes, 52 branches in 7 months
equationsstochastic-differential-equationsdifferentialequationsordinary-differential-equationsdde
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