Peter Prettenhofer
Principal Research Engineer at Neo Cybernetica
Vienna, Austria
Summary
🤩
Rockstar🎓
Top SchoolPeter Prettenhofer is a Principal Research Engineer based in Vienna with 16 years of experience bridging research and product engineering in ML and information systems. He has held senior engineering and leadership roles at DataRobot and now Neo Cybernetica, combining hands-on data science and software engineering with team and product leadership. His background includes academic research in information extraction and adaptive retrieval at Graz University of Technology and Bauhaus-Universität Weimar, bringing rigorous experimental methods to production problems. An active contributor to scikit-learn, he has improved decision-tree and gradient-boosting components (including Huber loss support and best-first tree growing), demonstrating deep expertise in ML algorithms. Trained in software development and business management, he excels at translating complex models into deployable systems that meet business needs. Colleagues rely on him for marrying research-grade ideas with practical, scalable implementations.
16 years of coding experience
15 years of employment as a software developer
Dipl. Ing. Software Development & Management, Dipl. Ing. Software Development & Management at Technische Universität GrazComputer Science, Computer Science at Norwegian University of Science and Technology
Stackoverflow
1,961reputation
208kreached
19answers
11questions
machine-learning
top-5%
scikit-learn
top-5%
Github Skills (20)
python10
scikit10
machine-learning10
gradient-boosting10
regression10
scikit-learn10
decision-tree10
data-science9
algorithm9
algorithms9
implement9
statistical-models9
random-forest6
amazon-ec26
large-data6
Programming languages (5)
Github contributions (5)
scikit-learn/scikit-learn
Oct 2010 - Mar 2015
scikit-learn: machine learning in Python
Role in this project:
Data Scientist
Data ScientistContributions:879 commits, 2 PRs, 1 push in 4 years 5 months
Contributions summary:Peter contributed to the scikit-learn library, primarily focused on enhancements to the tree module, as evidenced by their commits. Their work included adding support for the Huber loss function in robust regression within the Gradient Boosting framework. Additionally, they corrected issues related to impurity calculation, and updated examples to reflect the changes, and implemented best-first tree growing through the max_leaf_nodes parameter. Their contributions involved working with decision trees, loss functions, and algorithms used in machine learning.
data-analysispythonstatisticsdata-sciencelearn-machine-learning
pprett/nut
Oct 2010 - May 2014
Contributions:79 commits in 3 years 7 months
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