InstituteStaff
Sarah Krebs

Sarah Krebs

Sarah Krebs
Address
Appelstraße 9a
30167 Hannover
Building
Room
Sarah Krebs
Address
Appelstraße 9a
30167 Hannover
Building
Room

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

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

    2017 - 2018
    Data
    Scientist, Know-Center GmbH

  • Education

    2022 - Present
    PhD Candidate, Leibniz University Hannover

    2015 - 2018
    Master of Science, Physics, TU Dresden

    2012 - 2015
    Bachelor of Science, Physics, TU Dresden

Publications

Projects

  • ERC Starting Grant: Interactive and Explainable Human-Centered AutoML
    Trust and interactivity are key factors in the future development and use of automated machine learning (AutoML), supporting developers and researchers in determining powerful task-specific machine learning pipelines, including pre-processing, predictive algorithm, their hyperparameters and--if applicable--the architecture design of deep neural networks. Although AutoML is ready for its prime time after it achieved impressive results in several machine learning (ML) applications and its efficiency improved by several orders of magnitudes in recent years, democratization of machine learning via AutoML is still not achieved. In contrast to previously purely automation-centered approaches, ixAutoML is designed with human users at its heart in several stages. First of all, the foundation of trustful use of AutoML will be based on explanations of its results and processes. Therefore, we aim for: (i) Explaining static effects of design decisions in ML pipelines optimized by state-of-the-art AutoML systems. (ii) Explaining dynamic AutoML policies for temporal aspects of dynamically adapted hyperparameters while ML models are trained. These explanations will be the base for allowing interactions, bringing the best of two worlds together: human intuition and generalization capabilities for complex systems, and efficiency of systematic optimization approaches for AutoML. Concretely, we aim for: (iii) Enabling interactions between humans and AutoML by taking human's latent knowledge into account and learning when to interact. (iv) Building first ixAutoML prototypes and showing its efficiency in the context of Industry 4.0. Perfectly aligned with the EU's AI strategy and recent efforts on interpretability in the ML community, we strongly believe that this timely human-centered ixAutoML will have a substantial impact on the democratization of machine learning.
    Led by: Prof. Dr. Marius Lindauer
    Team: AutoML
    Year: 2022
    Funding: EU
    Duration: 2022-2027