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".

Links

Google Scholar

Leveraging AutoML for Sustainable Deep Learning: A Multi- Objective HPO Approach on Deep Shift Neural Networks

Verfasst von

Leona Hennig, Marius Lindauer

Details

Organisationseinheit(en): Institut für Künstliche Intelligenz
Fachgebiet Maschinelles Lernen
Typ: Aufsatz in Konferenzband
Publikationsdatum: 2025
Publikationsstatus: Elektronisch veröffentlicht (E-Pub)
Peer-reviewed: Ja
Elektronische Version(en): https://openreview.net/pdf?id=vk7b11DHcW (Zugang: Offen )
PDF: PDF