Research GroupScience and Probabilistic Intelligence
The group for Science and Probabilistic Intelligence (SPIN) combines foundational research on probabilistic AI with applied research in science.
We work on a range of scientific domains, aiming to drive discovery through machine learning. Outside of such applications, our AI research focuses on generative modeling, inverse problems and simulation-based inference, aiming to develop efficient, accurate and reliable methods.
Maximilian Dax is a postdoctoral researcher at ETH Zurich and the ELLIS Institute Tübingen and a member of the LIGO Scientific Collaboration. He completed his PhD at the Max Planck Institute for Intelligent Systems in Tübingen under supervision of Bernhard Schölkopf and interned at Google Research. His research focuses on probabilistic inference, generative modeling and density estimation, with an emphasis on scientific applications. Together with his collaborators, he developed DINGO, a leading machine learning approach for gravitational-wave data analysis.