K Olofsson
Scientist Special Projects at General Atomics
California, United States
Summary
👤
Senior🎓
Top SchoolK Olofsson is a scientist and applied machine learning engineer with eight years of experience translating advanced control theory and signal processing research into deployable solutions at General Atomics. Holding a PhD in Physical Electrotechnology from KTH, he has progressed from postdoctoral research on distributed-parameter system control to leading special projects that blend experimental physics, trajectory reconstruction, and event classification. He contributes to prominent open-source ML infrastructure — notably adding cross-entropy objectives and KL-divergence support to Microsoft's widely used LightGBM — showing a practical focus on performant, production-ready algorithms. Based in California, he combines academic rigor with hands-on engineering across simulation, control, and data-driven modeling. Colleagues describe him as someone who bridges deep theory and pragmatic implementation, often improving numerical robustness and edge-case behavior in mature codebases.
8 years of coding experience
9 years of employment as a software developer
PhD Physical Electrotechnology, PhD Physical Electrotechnology at KTH Royal Institute of TechnologyMSc Applied Physics and Electrical Engineering, MSc Applied Physics and Electrical Engineering at Linköping University
Swedish, English, matlab, c/c++, German, r
Github Skills (8)
gbm10
machine-learning10
lightgbm10
gradient-boosting10
cprogramming-language9
c-language9
data-mining9
python8
Programming languages (1)
Github contributions (5)
microsoft/LightGBM
Jul 2017 - Sep 2017
A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.
Role in this project:
ML Engineer
ML EngineerContributions:5 commits, 5 PRs, 21 comments in 2 months
Contributions summary:K primarily contributed to the implementation of cross-entropy metrics and objectives within the LightGBM framework, including Kullback-Leibler divergence. They added functionality for customizable "boost-from-average" and addressed issues related to the Poisson regression objective. Furthermore, the user fixed integer signed/unsigned compare warnings and made minor code style fixes.
kagglepythondata-mininglightgbmmicrosoft
olofer/mpfss
Jan 2018 - Nov 2018
Contributions:16 commits, 12 pushes, 1 branch in 10 months
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