Michael Pürrer


Dr. Pürrer is an Adjunct Assistant Professor in the Department of Physics and a Computational Scientist in the Center for Research Computing.


He is an expert in source modeling of gravitational-wave (GW) signals and GW data analysis with Bayesian inference. He studies GWs emitted by binary black hole mergers, as well as binary neutron star and neutron star black hole coalescences. His current focus is simulation-based inference using deep learning algorithms for conditional density estimation. He has been a member of the NSF-funded LIGO Scientific Collaboration since 2013. Dr. Pürrer has extensive experience in parallel and scientific computing, statistical learning, and deep learning techniques.

He has published 37 small author-list research papers, and more than 150 collaboration papers in top international journals, most of which with the LIGO-Virgo-KAGRA (LVK) collaboration. He has led two LVK collaboration papers, including the GWTC-1 catalog of compact binaries paper which details the results from the first two observing runs of LIGO.


Dr. Pürrer obtained his PhD from the University of Vienna, Austria in 2007.

Selected Publications

An auto-generated list of Dr. Pürrer‘s publications can be found on the Astrophysics data system website.