Michael Osthege
Lead Automation Engineer
Jülich, North Rhine-Westphalia, Germany
Summary
🤩
Rockstar🎓
Top SchoolMichael Osthege is a Lead Automation Engineer with 12 years of experience at the intersection of bioprocess engineering, laboratory automation, and probabilistic data science. He leads automation efforts at Mibiolab after a PhD focused on digitalization and Bayesian analysis of bioprocesses at Forschungszentrum Jülich, combining hands-on lab systems integration with statistical modeling. An active open-source contributor to heavyweight projects like PyMC, PyTensor and Aesara, he brings backend rigor—fixing broadcasting/log-probability bugs, strengthening type hints, and improving config robustness—to scientific Python tooling. Michael’s background in systems biology and molecular biotechnology, plus applied work in CNNs for microscopy, gives him a rare blend of wet-lab domain knowledge and production-quality software engineering. He’s particularly adept at turning probabilistic models into reliable automation pipelines that accelerate experimental design and interpretation.
12 years of coding experience
Master's degree, Systems Biology, Master's degree, Systems Biology at Ruprecht-Karls-Universität HeidelbergBachelor of Science (B.Sc.), Molecular and Applied Biotechnology, Bachelor of Science (B.Sc.), Molecular and Applied Biotechnology at RWTH Aachen University
German, English
Stackoverflow
621reputation
459kreached
10answers
1question
Github Skills (29)
probabilistic-programming10
python10
mypy10
statistics10
configuration-management10
numpy10
mcmc10
pymc10
bayesian10
refactoring10
bayesian-inference10
theano10
py9
code-optimization9
statistic9
Programming languages (16)
Github contributions (5)
pymc-devs/pymc
Aug 2017 - Jan 2023
Bayesian Modeling and Probabilistic Programming in Python
Role in this project:
Back-end Developer & Data Scientist
Back-end Developer & Data ScientistContributions:13 releases, 1004 reviews, 287 commits in 5 years 5 months
Contributions summary:Michael contributed to the Bayesian Modeling and Probabilistic Programming in Python project, pymc. The user's commits focused on fixing issues related to broadcasting and log probability computations within the distributions of the project. The user also worked on implementing new distribution-related features, such as creating distributions and related tests. The contributions include work on the DiscreteUniform function and the plotting of KDE plots to improve visualization.
pythonbayesian-inferencestatistical-inferencemachine-learningprobabilistic-programming
pymc-devs/pytensor
Nov 2022 - Jan 2023
PyTensor allows you to define, optimize, and efficiently evaluate mathematical expressions involving multi-dimensional arrays.
Role in this project:Backend Developer
Backend DeveloperContributions:3 releases, 117 reviews, 24 commits in 1 month
Contributions summary:Michael primarily focused on improving the codebase of the PyTensor project by addressing type hints, updating dependencies, and fixing syntax issues. They upgraded the mypy version and resolved related errors, indicating a focus on code quality and static analysis. Additionally, the user linked Aesara and integrated it into code examples. Furthermore, the user contributed to code documentation.
python-librarymulti-dimensionalpythonmathematical-expressionsdimensional
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