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 scalable approximate Bayesian inference methods (especially variational methods) and on deep probabilistic models, with applications to neural data compression and natural sciences. We also use ideas from Bayesian inference to work towards a radically new kind of equitable distributed machine learning on decentralized networks such as blockchains. Our research is at the intersection of probabilistic models, efficient inference algorithms, and novel powerful approximation techniques. The latter are often inspired by ideas from natural sciences due to my personal background in statistical physics.

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.


1/2021 I feel honored to be part of this year's jury for Jugend Forscht, a prestigious science fair under the auspices of the President of Germany. This contest truly encourages creativity—the only assignment for participating high school students is: "do something interesting".
11/2020 I've been appointed Professor of Data Science and Machine Learning at University of Tübingen, the Cluster of Excellence "Machine Learning: New Perspectives for Science", and Tübingen AI Center. I am very excited to build up a new research group here at one of the most vibrant environments for machine learning research in Europe. Please if you are interested in joining my group as a PhD or postdoctoral researcher.
9/2020 Two NeurIPS papers accepted: "Improving Inference for Neural Image Compression" and "User-Dependent Neural Sequence Models for Continuous-Time Event Data". Congratulations to the students Yibo Yang and Alex Boyd!
9/2020 Chemical Communications has accepted our paper "Hybridizing physical and data-driven prediction methods for physicochemical properties".

Machine Learning Publications

Representative publications include improvements to black box variational inference (BBVI) and applications of BBVI to time series models, to deep neural data compression, 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.