November 27-December 1st 2023
IAP, Paris / Flatiron institute, New York
Scientific rationale
The 2023 conference on Machine Learning in astronomical surveys intend to critically review new techniques in the Machine Learning methods for astronomy.
In order to bring together the widest possible community, while limiting the carbon impact, this conference will be organized in hybrid mode and on two physical sites simultaneously, at the IAP in Paris and at the CCA/Flatiron Institute in New York. For the same reason, we encourage the participants to travel by train if possible. The workshop will take place from Monday to Friday in the afternoon only during hours compatible with both time zones.
Invited reviewers and panellists
Our final list of invited scholars is still evolving beyond the confirm list here-in-below.
Reviewers (confirmed):
- In person IAP (Paris):
- Jens Jasche (Stockholm University): ML and Bayesian inference in cosmology
- Tomasz Kacprzak (ETH Zurich/ Swiss Data Science Center): ML and galaxy surveys
- Marylou Gabrié (CMAP, Polytechnique): Adaptive techniques for Monte Carlo and generative models
- Miles Cranmer (Cambridge University): Symbolic regression
- In person CCA (New York):
- Soledad Villar (JHU): Symmetries in deep learning
- Rupert Croft (CMU): Deep learning and numerical simulations
- David Shih (Rutgers University): Domain adaptation
Invited debaters (confirmed):
- Nabila Aghanim (IAS, Orsay)
- Pierre Casenove (CNES)
- Kyunghyun Cho (NYU)
- Aleksandra Ciprijanovic (Fermilab)
- Helena Domínguez Sánchez (CEFCA)
- Torsten Ensslin (MPA)
- David Hogg (NYU / Flatiron Institute)
- François Lanusse (LCS, CNRS)
- Luisa Lucie-Smith (MPA)
- Henry Joy McCracken (IAP, CNRS)
- Douglas Scott (UBC)
- David Spergel (Simons Foundation)
- Lawrence Saul (Flatiron Institute)
- Licia Verde (ICC-UB)
- David H. Weinberg (Ohio State University)
Acknowledgements
Credit image: Jean Mouette (IAP)
We acknowledge the financial support of the following agencies, institutes and national initiatives : (CNES, Simons Foundation, PNCG, IAP, DIM ORIGINES, Région Ile-de-France, SCAI, Learning the Universe).
