Johannes Kulick
Sr. Robotics Systems Engineer at Amazon
Berlin, Germany
Summary
👤
Senior🎓
Top SchoolJohannes Kulick is a senior robotics systems engineer and machine learning researcher with 14 years of experience building and scaling robotic manipulation systems at Amazon and earlier academic labs. He combines deep expertise in active learning, Bayesian inference and exploration strategies with hands-on software development in Python and C++ (plus ROS) to move research into production. His contributions to scientific tooling include implementing the Dirichlet distribution in SciPy and refining Bayesian changepoint detection libraries, reflecting care for correctness, testing and reproducibility. Johannes has published and organized workshops at top AI and robotics venues (IJCAI, IROS, ICRA) and repeatedly bridged research and engineering as both a program chair and a code contributor. He also brings practical security and backend experience from open-source work on the terminal mail client alot, where he improved GPG encryption features and UX. Based in Berlin, he pairs rigorous doctoral-level research training with a pragmatic, product-oriented approach to robot systems.
14 years of coding experience
13 years of employment as a software developer
Master of Science (M.Sc.) Computer Science, Master of Science (M.Sc.) Computer Science at Freie Universität BerlinDr. rer. nat Computer Science, Dr. rer. nat Computer Science at University of StuttgartMachine Learning Summer SchoolErasmus, Erasmus at Uppsala University
German, English, Swedish
Stackoverflow
1,670reputation
104kreached
27answers
14questions
Github Skills (23)
algorithm10
algorithms10
scipy10
python10
numpy10
statistical-models10
cryptography10
bayesian-methods10
jupyter-notebook10
scientific-computing10
e-mail8
send-email8
sendmail8
seaborn7
machine-learning7
Programming languages (9)
Github contributions (5)
hildensia/bayesian_changepoint_detection
Apr 2014 - Nov 2022
Methods to get the probability of a changepoint in a time series.
Role in this project:
Data Scientist
Data ScientistContributions:1 release, 8 reviews, 27 commits in 8 years 8 months
Contributions summary:Johannes implemented and refined Bayesian changepoint detection algorithms. Their contributions include both online and offline changepoint detection methods, as evidenced by the creation of corresponding Python files. Furthermore, the user created an example Jupyter notebook to demonstrate how to use the implemented methods. They also incorporated improvements, such as parameter adjustments and using Seaborn for visualizations, demonstrating an iterative approach to algorithm development and presentation.
probabilitytime-seriestime-series-analysischangepoint
scipy/scipy
Jul 2014 - Mar 2018
SciPy library main repository
Role in this project:Data Scientist
Data ScientistContributions:21 commits, 1 PR, 12 comments in 3 years 8 months
Contributions summary:Johannes contributed significantly to the implementation of the Dirichlet distribution within the SciPy library, a core component for statistical and scientific computing. Their work involved defining the distribution's functions like PDF, logpdf, mean, and variance, as well as adding tests to ensure the correctness of the implementation. Furthermore, the user addressed bugs, incorporated documentation, and added the Dirichlet distribution to the release notes, highlighting a focus on feature implementation and testing.
scipypythonscientific-computing
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