Maximilian Dax

I’m a machine learning researcher and physicist. I lead the Science and Probabilistic Intelligence (SPIN) group at the ELLIS Institute and the Max Planck Institute for Intelligent Systems. We combine 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.

Feel free to reach out if you want to discuss research ideas or potential collaborations.

We are hiring. If you are interested in joining our group as a PhD student, postdoc or intern, feel free to reach out via email. Please include a CV and transcripts of your grades when first reaching out. We will admit PhD students through the ELLIS, CLS and IMPRS programs, the application deadlines are usually around November.

news

Sep 01, 2025	I’m joining ELLIS and MPI as a PI and independent research group leader. Read the announcement here.
Jul 17, 2025	I defended my PhD. Thanks to my advisors Bernhard Schölkopf, Jakobs Macke and Stephen Green and to all my collaborators for a great time at MPI!
Mar 05, 2025	New Nature paper on ML inference of neutron star mergers. Press release, Nature News & Views.

selected publications

Nature

Real-time inference for binary neutron star mergers using machine learning

Maximilian Dax, Stephen R. Green, Jonathan Gair, and 7 more authors

Nature, Mar 2025

DOI arXiv Bib HTML PRESS PDF

@article{Dax:2024mcn,
  author = {Dax, Maximilian and Green, Stephen R. and Gair, Jonathan and Gupte, Nihar and P{\"u}rrer, Michael and Raymond, Vivien and Wildberger, Jonas and Macke, Jakob H. and Buonanno, Alessandra and Sch{\"o}lkopf, Bernhard},
  title = {{Real-time inference for binary neutron star mergers using machine learning}},
  eprint = {2407.09602},
  archiveprefix = {arXiv},
  primaryclass = {gr-qc},
  reportnumber = {LIGO-P2400294},
  doi = {10.1038/s41586-025-08593-z},
  journal = {Nature},
  volume = {639},
  number = {8053},
  pages = {49--53},
  year = {2025},
  month = mar,
  url = {https://www.nature.com/articles/s41586-025-08593-z},
  dimensions = {true},
}

PRL

Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference

Maximilian Dax, Stephen R. Green, Jonathan Gair, and 5 more authors

Phys. Rev. Lett., Apr 2023

DOI arXiv Bib HTML PRESS PDF

@article{Dax:2022pxd,
  author = {Dax, Maximilian and Green, Stephen R. and Gair, Jonathan and P{\"u}rrer, Michael and Wildberger, Jonas and Macke, Jakob H. and Buonanno, Alessandra and Sch{\"o}lkopf, Bernhard},
  title = {{Neural Importance Sampling for Rapid and Reliable Gravitational-Wave Inference}},
  eprint = {2210.05686},
  archiveprefix = {arXiv},
  primaryclass = {gr-qc},
  reportnumber = {LIGO-P2200297},
  doi = {10.1103/PhysRevLett.130.171403},
  journal = {Phys. Rev. Lett.},
  volume = {130},
  number = {17},
  pages = {171403},
  year = {2023},
  month = apr,
  url = {https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.130.171403},
  dimensions = {true},
}

PRL

Real-Time Gravitational Wave Science with Neural Posterior Estimation

Maximilian Dax, Stephen R. Green, Jonathan Gair, and 3 more authors

Phys. Rev. Lett., Apr 2021

DOI arXiv Bib HTML PRESS PDF

@article{Dax:2021tsq,
  author = {Dax, Maximilian and Green, Stephen R. and Gair, Jonathan and Macke, Jakob H. and Buonanno, Alessandra and Sch\"olkopf, Bernhard},
  title = {{Real-Time Gravitational Wave Science with Neural Posterior Estimation}},
  eprint = {2106.12594},
  archiveprefix = {arXiv},
  primaryclass = {gr-qc},
  reportnumber = {LIGO-P2100223},
  doi = {10.1103/PhysRevLett.127.241103},
  journal = {Phys. Rev. Lett.},
  volume = {127},
  number = {24},
  pages = {241103},
  year = {2021},
  url = {https://journals.aps.org/prl/abstract/10.1103/PhysRevLett.127.241103},
  dimensions = {true},
}