Our online seminar series begins on February 25, at 5:30 PM (CET), with a talk by Maximilian Dax (ELLIS Institute Tübingen and Max Planck Institute for Intelligent Systems).
About the speaker:
Maximilian Dax is a Principal Investigator at the ELLIS Institute Tübingen and the Max Planck Institute for Intelligent Systems, where he leads the research group for Science and Probabilistic Intelligence. His research focuses on probabilistic machine learning and its application in science. Before joining ELLIS, he completed his PhD at the Max Planck Institute for Intelligent Systems and the University of Tübingen, and spent time at ETH Zurich and Google Research
Title:
Real-Time Gravitational-Wave Inference with Probabilistic Machine Learning
Abstract:
Gravitational-wave (GW) astronomy promises groundbreaking discoveries in the coming decades, but its progress is bottlenecked by the computational challenges of large-scale and real-time data analysis. I will present DINGO, a machine learning approach for fast and accurate GW inference that addresses these challenges. DINGO trains generative neural networks to directly estimate probability distributions over GW source parameters. I will explain the core ideas behind DINGO and highlight several machine learning techniques that we developed to adapt modern simulation-based inference to the challenging field of GW data analysis.