Sarah Segel – Institute of Artificial Intelligence

Phone

+49 511 762 12413

Email

s.segel@ai.uni-hannover.de

Address

Welfengarten 1
30167 Hannover

Building

1101

Room

b415

Phone

+49 511 762 12413

Email

s.segel@ai.uni-hannover.de

Address

Welfengarten 1
30167 Hannover

Building

1101

Room

b415

I am a PhD candidate interested in the intersection of automated machine learning (AutoML) and interpretable machine learning.

I received my master’s degree in Physics at TU Dresden in 2016. After working on the application of machine learning to different problems in the industry for a few years, I started my PhD at Leibniz University under the supervision of Prof. Marius Lindauer in October 2022.

Research Interests

Automated machine learning
Interpretable machine learning

Curriculum Vitae

Working Experience

2022 - Present
Doctoral Researcher, Leibniz University Hannover

2018 - 2022
Data Scientist, OSP (Otto Group Solution Provider) GmbH

2017 - 2018
Data Scientist, Know-Center GmbH
Education

2022 - Present
PhD Student, Leibniz University Hannover

2015 - 2018
Master of Science, Physics, TU Dresden

2012 - 2015
Bachelor of Science, Physics, TU Dresden

Links

Google Scholar

DBLP

Twitter

Website

Publications

Showing results 1 - 2 out of 2

2024

Tornede, A., Deng, D., Eimer, T., Giovanelli, J., Mohan, A., Ruhkopf, T., Segel, S., Theodorakopoulos, D., Tornede, T., Wachsmuth, H., & Lindauer, M. (2024). AutoML in the Age of Large Language Models: Current Challenges, Future Opportunities and Risks. Transactions on Machine Learning Research. https://doi.org/10.48550/arXiv.2306.08107

More...

2023

Segel, S., Graf, H., Tornede, A., Bischl, B., & Lindauer, M. (2023). Symbolic Explanations for Hyperparameter Optimization. In AutoML Conference 2023 PMLR. Advance online publication. https://openreview.net/forum?id=JQwAc91sg_x