PhD Student

Lennart Purucker

Postal address

Institut für Informatik
Albert-Ludwigs-Universität Freiburg
Sekretariat Hutter/Maschinelles Lernen
Georges-Köhler-Allee 074
79110 Freiburg, Germany


Building 074, Room 00-012


I am a Ph.D. student at the University of Freiburg, Germany, supervised by Frank Hutter. My Ph.D. position is part of the Small Data Initiative (CRC 1597, Project C05). My research interest is in the field of artificial intelligence, with a focus on automated machine learning, ensemble learning, deep learning, and meta-learning (for small data).

I completed my bachelor’s degree in applied computer science in 2019 at the DHBW Stuttgart and my master’s degree in 2021 in computer science at the RWTH Aachen. From November 2021 to August 2023, I worked as a research assistant at the University of Siegen. From August 2023 to November 2023, I was an applied scientist intern at AWS as part of the AutoGluon team.

Community Involvement




Wegmeth, Lukas; Vente, Tobias; Purucker, Lennart; Beel, Joeran

The Effect of Random Seeds for Data Splitting on Recommendation Accuracy Conference

Perspectives on the Evaluation of Recommender Systems Workshop (PERSPECTIVES 2023), co-located with the 17th ACM Conference on Recommender Systems, 2023.

Purucker, Lennart; Beel, Joeran

CMA-ES for Post Hoc Ensembling in AutoML: A Great Success and Salvageable Failure Conference

AutoML Conference 2023, 2023.

Purucker, Lennart; Schneider, Lennart; Anastacio, Marie; Beel, Joeran; Bischl, Bernd; Hoos, Holger

Q(D)O-ES: Population-based Quality (Diversity) Optimisation for Post Hoc Ensemble Selection in AutoML Conference

AutoML Conference 2023, 2023.


Purucker, Lennart; Stamm, Felix; Lemmerich, Florian; Beel, Joeran

Estimating the Pruned Search Space Size of Subgroup Discovery Inproceedings

In: 2022 IEEE International Conference on Data Mining (ICDM), 2022.

Purucker, Lennart; Beel, Joeran

Assembled-OpenML: Creating Efficient Benchmarks for Ensembles in AutoML with OpenML Conference

First Conference on Automated Machine Learning (Late-Breaking Workshop), 2022.