Robert Bamler
Robert Bamler

Professor of Data Science and Machine Learning at University of Tübingen, Germany, and member of the Cluster of Excellence "Machine Learning: New Perspectives for Science".

Robert Bamler

I am a Professor of Data Science and Machine Learning at University of Tübingen, Germany, and a member of the Cluster of Excellence "Machine Learning: New Perspectives for Science" and Tübingen AI Center.

My group performs research on deep generative models (including large language models), resource-efficient inference, model compression, and applications of probabilistic machine-learning methods to natural sciences. We typically focus on developing new generic algorithms and approximation techniques rather than exploring a large space of model architectures, and we aim at developing practical methods guided by computational constraints and theoretical insights. The latter are usually based in statistics, information theory, or come from idea transfer from my background in theoretical physics.

Find out more about my group at our group website.

I joined University of Tübingen and the Cluster of Excellence for Machine Learning in November 2020. Before this, I was a postdoctoral scholar in the statistical machine learning group of UC Irvine lead by Stephan Mandt, and before that I was a machine learning researcher at Disney Research in Pittsburgh and Los Angeles. I received my PhD in theoretical statistical and quantum physics from University of Cologne in 2016, advised by Achim Rosch and with support from German Telekom Foundation.

Machine Learning Publications

Representative publications include improvements to black box variational inference (BBVI) and applications of BBVI to neural data compression, to time series models, and to natural sciences, as well as resource efficient learning algorithms. For more publications, see list below.

Physics Publications

Talks & Video Abstracts

Below is a selection of talks and video abstracts where either recordings or slides are available.