Aaron Klein

The goal of my research is to develop machine learning approaches that can configure themselves automatically by learning from past data. In the long term, I hope that my work will contribute to fulfilling the promise of artificial intelligence and lead to complete end-to-end learning systems.

More specifically, my research interests include:

• Automated machine learning
• Neural architecture search
• Bayesian hyperparameter optimization
• Model compression of large language models
• Automated prompt engineering

short bio

I am the head of the AutoML research group at ScaDS.AI (Center for Scalable Data Analytics and Artificial Intelligence) in Leipzig. I am also a co-host of the virtual AutoML Seminar as part of the ELLIS units in Berlin and Freiburg. Alongside my collaborators, I lead the development of the open-source library SyneTune for large-scale hyperparameter optimization and neural architecture search.

Until 2024, I worked as an applied scientist at AWS, where I was part of the long-term science team of SageMaker, AWS’s machine learning cloud platform, and the science team of Amazon Q, the GenAI assistant of AWS. Prior to that, I finished my PhD at the University of Freiburg under the supervision of Frank Hutter in 2019. In 2022, my co-authors and I won the best paper award at the AutoML Conference. My collaborators from the University of Freiburg and I won the ChaLearn AutoML Challenge in 2015. I co-organized the workshop on neural architecture search at ICRL 2020 and ICLR 2021, respectively and served as local chair for the AutoML Conference in 2023. I also regularly serve as a reviewer for ICML, ICLR, NeurIPS, TMLR, and JMLR.