Aaron Klein

I lead a research group at the ELLIS Institute Tübingen as part of OpenEuroLLM, a European initiative developing a family of foundation models for European languages. Our team focuses on advancing automated methods for large-scale pre-training of large language models.

I also co-host the virtual AutoML Seminar, organized by the ELLIS units in Berlin and Freiburg.

My research interests include:

• AutoML, which aims to progressively automate various stages of the machine learning pipeline. Within AutoML, I primarily work on hyperparameter optimization and neural architecture search.

• Blackbox Optimization (or gradient-free optimization), with a focus on Bayesian optimization methods.

• Model compression of large language models to reduce resource consumption during inference.

Previously, I headed the AutoML research group at ScaDS.AI (Center for Scalable Data Analytics and Artificial Intelligence) in Leipzig.

Until 2024, I worked as an applied scientist at AWS, where I was part of the long-term science team behind SageMaker, AWS’s machine learning cloud platform, as well as the science team for Amazon Q, AWS’s generative AI assistant.

I earned my PhD at the University of Freiburg in 2019 in the machine learning lab under the supervision of Frank Hutter. In 2022, my co-authors and I received the Best Paper Award at the AutoML Conference. Earlier, in 2015, my collaborators from the University of Freiburg and I won the ChaLearn AutoML Challenge.

I have co-organized the Neural Architecture Search workshop at ICRL 2020 and ICLR 2021, and served as the local chair for the AutoML Conference 2023. I regularly review for top-tier venues including ICML, ICLR, NeurIPS, TMLR, and JMLR.