Justus Schwabedal
Software Engineer at Deloitte
Dresden, Saxony, Germany
Summary
👤
Senior🎓
Top SchoolJustus Schwabedal is a software engineer and complexity researcher with 11 years of experience bridging academic rigor and production software, currently contributing at Deloitte from Dresden. He holds a PhD in Nonlinear Dynamics and has a track record of applying machine learning and signal-processing expertise to neuroscience and sleep-research projects, from automated sleep staging to online stimulation studies. As a longtime open-source contributor to scikit-optimize, he focuses on visualization and usability improvements for Bayesian optimization tooling. Comfortable leading technical teams and hands-on engineering, he has served as CTO, lead engineer, and senior scientist across startups and research labs. Colleagues benefit from his test-first philosophy—summed up by his Bruce Eckel-inspired motto—and his ability to translate complex models into robust, well-documented code.
11 years of coding experience
10 years of employment as a software developer
Doctor of Philosophy (PhD), Nonlinear Dynamics, Doctor of Philosophy (PhD), Nonlinear Dynamics at Universität PotsdamMaster of Science (MSc), Physics, Master of Science (MSc), Physics at Rheinische Friedrich-Wilhelms-Universität Bonn
Stackoverflow
1reputation
0reached
1answer
0questions
Github Skills (13)
scikit10
machine-learning10
visualization10
visualizations10
optmization10
python10
optimisation10
optimization10
scikit-learn10
numpy9
hyperparameter-optimization9
matplotlib8
amazon-sns6
Programming languages (8)
Github contributions (5)
scikit-optimize/scikit-optimize
Sep 2017 - Sep 2017
Sequential model-based optimization with a `scipy.optimize` interface
Role in this project:
Data Scientist
Data ScientistContributions:5 commits, 6 PRs, 13 comments in 16 days
Contributions summary:Justus primarily contributes to the visualization aspects of the `scikit-optimize` library, focusing on plotting functionality. They implemented and refined plots related to objective functions and evaluation visualizations, particularly handling log-scale transformations for dimensions with log-uniform priors. Furthermore, the user modified existing plot functions to align with code style and usability guidelines and incorporated updates suggested by collaborators. They also addressed documentation details.
pythonbayesoptscipymodel-based-optimizationbayesian-optimization
jusjusjus/wxArt
Sep 2016 - Jun 2017
Contributions:131 commits, 20 PRs, 69 pushes in 9 months
pythonartwxpython
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