Nikhil Yewale
PHD Student at Indian Institute of Technology, Bombay
India
Summary
👤
Senior🎓
Top SchoolNikhil Yewale is a PhD student and researcher in fluid mechanics and computational science with nine years of experience bridging theoretical analysis and applied CFD. He has developed C++/OpenFOAM utilities for hemodynamic assessment of intracranial aneurysms, automated HPC workflows, and applied supervised learning to clinical rupture‑risk problems. Comfortable with pen‑and‑paper derivations as well as Julia and MATLAB coding, he contributes to SciML/Catalyst.jl on reaction‑network APIs and tutorials, reflecting a strong interest in open‑source scientific software. His work spans spectral finite element methods, nonlinear interfacial waves, and practical tooling for simulations, showing an ability to move between deep theory and productionized code. Based in India, he combines academic rigor from IIT Madras and IIT Bombay with hands‑on implementation on HPC clusters and community software projects. An uncommon strength is his fluency in both low‑level C++ CFD engineering and high‑level Julia symbolic modeling, enabling cross‑cutting contributions to research and open source.
9 years of coding experience
Indian Institute of Technology MadrasIndian Institute of Technology BombayBachelor's Degree Civil Engineering, Bachelor's Degree Civil Engineering at Government College of Engineering Karad
English, Hindi, Marathi
Github Skills (170)
ode10
systems-biology10
high-performance10
stochastic-differential-equations10
julia10
biology10
dsl10
cas10
scientific-machine-learning10
simulation10
dynamical-systems10
reaction10
reaction-network10
sbml10
differential-equations10
Programming languages (7)
Github contributions (5)
yewalenikhil65/Catalyst.jl
Aug 2021 - May 2022
Chemical reaction network and systems biology interface for scientific machine learning (SciML). High performance, GPU-parallelized, and O(1) solvers in open source software
Contributions:13 commits, 14 PRs, 130 pushes in 8 months
reactiongpusolverschemicalbiology
yewalenikhil65/DiffEqFlux.jl
Mar 2021 - Mar 2024
Universal neural differential equations with O(1) backprop, GPUs, and stiff+non-stiff DE solvers, demonstrating scientific machine learning (SciML) and physics-informed machine learning methods
Contributions:2 PRs, 4 pushes, 1 comment in 3 years
scientific-machine-learningpartial-differential-equationsdifferentialodeordinary-differential-equations
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