Lennart Purucker
AI Scientist at The University of Freiburg
Freiburg im Breisgau, Baden-Württemberg, Germany
Summary
🤩
Rockstar🎓
Top SchoolLennart Purucker is an AI Scientist and PhD student specializing in automated machine learning and foundation models for tabular data, with four years of applied research and engineering experience. He develops tabular foundation models at Prior Labs while pursuing doctoral research at the University of Freiburg under Frank Hutter, blending cutting‑edge research with production-focused engineering. His contributions to AutoGluon—including deterministic bagged ensembles, dynamic stacking, and FastAI regression tweaks—reflect deep practical expertise in making AutoML systems more reliable and flexible. Past internships at AWS and roles at Universität Siegen and HPE show a consistent thread of productionizing ML and cloud-native systems from prototypes to robust tooling. Based in Freiburg, Germany, he is an active open-source contributor to AutoGluon, OpenML, and TabPFN, signaling a commitment to community-driven ML infrastructure. Notably, he focuses on the often-overlooked challenge of adapting foundation-model techniques specifically for tabular data at scale.
4 years of coding experience
3 years of employment as a software developer
Doktor (Ph.D.) Computer Science, Doktor (Ph.D.) Computer Science at The University of FreiburgBachelor of Science (B.Sc.) Computer Science, Bachelor of Science (B.Sc.) Computer Science at Baden-Wuerttemberg Cooperative State University (DHBW)Doktor (Ph.D.) Computer Science, Doktor (Ph.D.) Computer Science at Universität SiegenMaster of Science - MS Computer Science, Master of Science - MS Computer Science at RWTH Aachen University
German, English
Github Skills (15)
datatables10
pytorch10
machine-learning10
datatable10
automated-machine-learning10
python10
ensemble-learning10
tabular10
scikit-learn9
scikit9
deeplearning-ai9
deep-learning9
hyperparameter-optimization8
forecasting7
forecast7
Programming languages (7)
Github contributions (5)
autogluon/autogluon
Aug 2023 - Feb 2025
Fast and Accurate ML in 3 Lines of Code
Role in this project:
ML Engineer
ML EngineerContributions:28 reviews, 23 PRs, 58 comments in 1 year 6 months
Contributions summary:Lennart contributed to the AutoGluon project by implementing and refining machine learning model functionalities. They addressed deterministic predictions in bagged ensemble models, incorporating code changes to the core model structure. The user also added a clipping mechanism within the FastAI neural network models for regression problems. Additionally, the user modified the core trainer code to support dynamic stacking and control AutoGluon's repeated cross-validation behavior.
forecastingimage-textmlppythonmeta-learning
ISG-Siegen/assembled
May 2022 - Feb 2023
Contributions:2 releases, 62 commits, 60 pushes in 8 months
Find and Hire Top DevelopersWe’ve analyzed the programming source code of over 60 million software developers on GitHub and scored them by 50,000 skills. Sign-up on Prog,AI to search for software developers.