image Leonard Papenmeier

PhD Student

Bayesian Optimization, Gaussian Processes, Machine Learning, AutoML

CV.

Publications

2023 Leonard Papenmeier, Luigi Nardi, Matthias Poloczek Bounce: Reliable High-Dimensional Bayesian Optimization for Combinatorial and Mixed Spaces Preprint. Accepted for publication at the NeurIPS 2023 (main conference).
2023 Erik Hellsten*, Carl Hvarfner*, Leonard Papenmeier*, Luigi Nardi High-dimensional Bayesian Optimization with Group Testing Preprint. * Equal contribution.
2022 Leonard Papenmeier, Luigi Nardi, Matthias Poloczek Increasing the Scope as You Learn: Adaptive Bayesian Optimization in Nested Subspaces Advances in Neural Information Processing Systems 35, NeurIPS 2022, New Orleans.
2017 Leonard Papenmeier (then: Hövelmann), Christoph M. Friedrich Fasttext and Gradient Boosted Trees at GermEval-2017 on Relevance Classification and Document-level Polarity GermEval Shared Task on Aspect-based Sentiment in Social Media Customer Feedback (GSCL), Hamburg, 2017. My Bachelor's thesis resulted in this submission.

Education

Since 09/2020 PhD Student in Machine Learning at Lund University and Wallenberg AI, Autonomous Systems and Software Program, Sweden
2019 Exchange Semester (Data Science), NMBU, Ås, Norway
2017-2020 Master: Applied Computer Science, Ruhr-University, Bochum, Germany. Final grade: 95% (excellent)
2013-2017 Bachelor: Software Engineering, Dortmund University of Applied Sciences, Germany. Final grade: 1.6 (good)

Work Experience

2018-2020 Working student: Deep Learning and Computer Vision, img.ly GmbH, Bochum, Germany
2016-2018 Working student: Full-stack software development, adesso AG, Cologne, Germany
2013-2016 Apprenticeship: IT specialist, adesso AG & Chamber of Industry and Commerce (IHK), Germany

Teaching

2021-2024 Teaching Assistant: Artificial Intelligence (EDAP01), Lund University, Sweden
2022-2023 Teaching Assistant: Applied Machine Learning (EDAN96), Lund University, Sweden
2023 Teaching Assistant: Advanced Applied Machine Learning (EDAP30), Lund University, Sweden
2020-2021 Teaching Assistant: Applied Machine Learning (EDAN95), Lund University, Sweden

Reviewing

  • IEEE Transactions on Evolutionary Computation
  • Journal of Machine Learning Research (JMLR)
  • AutoML Conference 2023
  • ISAAC 2022

Theses

2020
Leonard Papenmeier Semantic Representations in Variational Autoencoders as a Model of the Visual System
Master Thesis
This thesis was written at the Institute of Neural Computation at Ruhr-University Bochum, Germany and supervised by Laurenz Wiskott and Zahra Fayyaz. The goal was to investigate the role of semantic representations in the visual system. I investigated the hypothesis that variational autoencoders (VAEs) learn semantic representations of images by analyzing latent representations of VAEs trained on different datasets. We could not find strong evidence for this hypothesis.

The thesis was graded with 100% (excellent).
2017
Leonard Papenmeier (then: Hövelmann) Sentiment Analysis Based on Word Embeddings: Possible Improvements and Transfer to the German Language
Bachelor Thesis
This thesis was written at Dortmund University of Applied Sciences and Arts, Germany and supervised by Christoph M. Friedrich. I used (then very recent) word embeddings to improve sentiment on German text. I also participated in the GermEval Shared Task on Aspect-based Sentiment in Social Media Customer Feedback (GSCL) 2017 (see publications) and achieved the best result for the German language on one subtask and the second-best result on another subtask.

The thesis was graded with 1.0 (very good).

Programming Languages & Frameworks

PyTorch, BoTorch, Torch, Python, TensorFlow, Keras, Java, JavaScript, C, Scala, C++, Assembler (x86), HTML, CSS, PHP, Apache Spark, Spring Framework, Angular, UML, TypeScript, JPA, JSF, JSP

Scholarships

2017-2020 Scholarship Program of the Friedrich-Ebert Foundation
Leonard Papenmeier

PhD Student

Bayesian Optimization, Gaussian Processes, Machine Learning, AutoML