Stijn De Waele
Data Scientist
Los Altos, California, United States
Summary
👤
Senior🎓
Top SchoolStijn De Waele is a data scientist with 10+ years of applied research experience bringing advanced algorithms from idea to business adoption across healthcare, semiconductors and oil & gas. He holds a PhD in signal processing and a master's in physics, and has driven technology transfers at Philips and ExxonMobil while co-authoring 50+ publications and 20+ US patents. His strengths include time series analysis, sensor fusion, automatic differentiation and robust statistical modeling, and he has improved production allocation and alarm-management systems with demonstrable commercial impact. Now at Google in Los Altos, he continues to bridge research and production engineering, contributing to open-source differentiable programming (Zygote.jl) by implementing custom adjoints for matrix exponentials and Lyapunov functions. Colleagues rely on him for turning complex mathematical ideas into deployable software and company-wide training material.
10 years of coding experience
14 years of employment as a software developer
Master's degree, Physics, 9.5 / 10, Master's degree, Physics, 9.5 / 10 at Delft University of Technology
Dutch, English
Github Skills (5)
automatic-differentiation10
machine-learning10
linear-algebra10
julia10
gradient10
Programming languages (5)
Github contributions (5)
FluxML/Zygote.jl
Apr 2019 - Dec 2019
21st century AD
Role in this project:
ML Engineer
ML EngineerContributions:9 commits, 4 PRs, 32 comments in 8 months
Contributions summary:Stijn focused on implementing and refining custom adjoints for automatic differentiation within the Zygote.jl framework. Their contributions involved adding adjoints for matrix exponential and Lyapunov functions, ensuring correct gradient calculations. They also addressed real-valued adjoints for real-valued matrix exponentials and improved test coverage to validate gradient accuracy. The user's work directly relates to improving the capabilities of Zygote.jl for differentiable programming.
control-flowautomatic-differentiationmachine-learningjulia-compilerjulia
sdewaele/miek-math
Feb 2017 - Oct 2017
Contributions:66 pushes, 1 branch in 8 months
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