Porträt von Leona Hennig, Doktorandin, mit langen dunklen Haaren, in einer weißen Bluse, lächelnd vor einem grauen Hintergrund. Porträt von Leona Hennig, Doktorandin, mit langen dunklen Haaren, in einer weißen Bluse, lächelnd vor einem grauen Hintergrund.
Leona Hennig
Adresse
Welfengarten 1
30167 Hannover
Gebäude
Raum
Porträt von Leona Hennig, Doktorandin, mit langen dunklen Haaren, in einer weißen Bluse, lächelnd vor einem grauen Hintergrund. Porträt von Leona Hennig, Doktorandin, mit langen dunklen Haaren, in einer weißen Bluse, lächelnd vor einem grauen Hintergrund.
Leona Hennig
Adresse
Welfengarten 1
30167 Hannover
Gebäude
Raum

Research Interests

My research interests revolve around theoretical development and practical application of statistical and Machine Learning methods, particularly in the context of Green AutoML. It is a field dedicated to developing environmentally sustainable and energy-efficient automated machine learning algorithms. I aim to leverage these techniques within industrial contexts to drive innovation across a variety of applications.

 

 

 

Curriculum Vitae

  • Working Experience

    2023 - Present
    Doctoral Researcher, Leibniz University Hannover

    2022 - 2023
    Doctoral Researcher, Volkswagen AG, Wolfsburg.

    2022
    Analytics Professional, Deloitte Consulding LLC, Dusseldorf.

    2021 - 2022
    Master's degree candidate, Internship and Thesis, IAV GmbH, Gifhorn.

  • Education

    2023 - Present
    Ph.D. Student at the Institute of Artificial Intelligence, Leibniz University Hannover

    2019 - 2022
    M.Sc. , Financial Mathematics, Technische Universität Braunschweig. Thesis: "Novelty Detection via Kernel Mean Embeddings".

    2016 - 2019
    B.Sc. , Financial Mathematics, Bielefeld University. Thesis: "Prediction of Customer Churn Using Machine Learning Algorithms".

Zeige Ergebnisse 1 - 4 von 4

2025


Becktepe J, Hennig L, Oeltze-Jafra S, Lindauer M. Auto-nnU-Net: Towards Automated Medical Image Segmentation. in International Conference on Automated Machine Learning 2025. 2025
Hennig L, Lindauer M. Leveraging AutoML for Sustainable Deep Learning: A Multi- Objective HPO Approach on Deep Shift Neural Networks. in Transactions on Machine Learning Research. 2025
Kocher N, Wassermann C, Hennig L, Seng J, Lindauer M, Hoos H et al. Guidelines for the Quality Assessment of Energy-Aware NAS Benchmarks. in Castanet 2025 Workshop on Challenges Advances and Sustainability in AI HPC Interaction: In conjunction with the 25th IEEE ACM International Symposium on Cluster Cloud and Internet Computing. 2025 Epub 2025. doi: 10.48550/arXiv.2505.15631

2024


Hennig L, Tornede T, Lindauer M. Towards Leveraging AutoML for Sustainable Deep Learning: A Multi-Objective HPO Approach on Deep Shift Neural Networks. in 5th Workshop on practical ML for limited/low resource settings. 2024 Epub 2024 Apr 2. doi: 10.48550/arXiv.2404.01965