Paresh Mathur
Chief Product Owner at ASML
Eindhoven, North Brabant, Netherlands
Summary
👤
Senior🎓
Top SchoolParesh Mathur is a pragmatic product leader with 14 years of experience blending deep technical know-how in photonics and lithography with user-centered product delivery at ASML. Currently Chief Product Owner based in Eindhoven, he has driven image optimization and wafermap modeling products while stepping into acting cPO roles to align business-line apps and engineering execution. His background spans R&D at TSMC on EUV source analytics and scanner big-data KPIs to hands-on research in ultrafast optics from IIT Kanpur, giving him rare domain expertise across hardware, algorithms, and production software. Paresh also contributes to open-source scientific tooling—adding automatic-differentiation support and rigorous tests to a Julia interpolation library—highlighting his attention to numerical correctness and reproducibility. Colleagues know him for an execution-focused, human-centric style that turns complex lithography problems into pragmatic, measurable product outcomes.
14 years of coding experience
9 years of employment as a software developer
Indian Institute of Technology KanpurBachelor's Degree, Electronics & Communication Engineering, Bachelor's Degree, Electronics & Communication Engineering at Shri U V Patel College of Engineering, Ganpat UniversityKendriya Vidyalaya Sangathan
English, Hindi, Gujarati, Chinese
Stackoverflow
302reputation
29kreached
0answers
5questions
Github Skills (14)
automatic-differentiation10
interpolation10
julia10
testing10
ziggy9
zig9
java6
verilog6
emulation6
pyserial6
rawinput6
python6
inheritance6
virtualization6
Programming languages (5)
Github contributions (5)
JuliaMath/Interpolations.jl
Apr 2021 - Apr 2021
Fast, continuous interpolation of discrete datasets in Julia
Role in this project:
Back-end Developer & QA Engineer / Test Automation Engineer
Back-end Developer & QA Engineer / Test Automation EngineerContributions:3 reviews, 11 commits, 3 PRs in 4 days
Contributions summary:Paresh's contributions focused on integrating automatic differentiation capabilities into the interpolation library. They implemented `ChainRulesCore.rrule` for automatic differentiation integration, enabling compatibility with AD libraries like Zygote. The user created and expanded a test suite to verify the correct behavior of the interpolated functions when used with automatic differentiation, including both 1D and 2D examples. They also refined the implementation with a docstring and handled a specific derivative case.
splinescontinuousdiscreteinterpolationjulia
rick2047/Interpolations.jl
Apr 2021 - Sep 2022
Fast, continuous interpolation of discrete datasets in Julia
Contributions:5 pushes, 2 branches in 1 year 5 months
continuousdiscreteinterpolationjuliadatasets
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