François Lanusse

I am a CNRS researcher working at the intersection between Deep Learning, Statistical Modeling, and Observational Cosmology. I am particularly interested in combining tools and methodologies from the field of Machine Learning (automatic differentiation, deep generative models, score-based high-dimensional inference) with physical modeling for the analysis of Stage IV Cosmological Surveys.

I am currently co-Chair of the LSST Informatics & Statistics Science Collaboration (LSST ISSC) and actively involved in the LSST Dark Energy Science Collaboration (LSST DESC).

Before my CNRS position, I was a postdoctoral fellow at the Berkeley Center for Cosmological Physics (BCCP) and with the Foundation of Data Analysis (FODA) institute at UC Berkeley, working with Prof. Uroš Seljak. Before that, I was a postdoctoral researcher in the McWilliams Center for Cosmology at Carnegie Mellon University, where I was working with Prof. Rachel Mandelbaum on weak gravitational lensing measurements and systematics, and interacted with both Statistics and Machine Learning departments while at CMU.

news

Sep 25, 2023	Starting a one year position at the Flatiron Institute! I will be based in New York City until Sept. 2024.
Jul 29, 2023	Check out all the great submissions that were accepted at the 2023 ICML ML4Astro Workshop in Hawai’i !
May 22, 2023	Great three days of machine learning and astrophysics at CCA for the Cosmic Connections Symposium. All recordings are here.
Feb 13, 2023	jax-cosmo paper is out on the arxiv thanks to the work of a lot of people! Fisher forecasts are now officially super easy!
Jan 30, 2023	Open PhD position at CEA Saclay to work with me on using generative modeling and autodiff to measure cosmic shear from space 🛰️. Application deadline March 10th.
Dec 12, 2022	Very cool project led by Yesukhei Jagvaral in collaboration with Rachel Mandelbaum on developing diffusion models for the SO3 Lie group to model galaxy intrinsic orientations in simulations. Full ML paper on this coming soon.
Nov 26, 2022	Pre-release of jaxDecomp, a JAX library providing bindings to NVIDIA’s cuDecomp adaptive pencil decomposition library for efficient multi-node/multi-gpu 3D FFTs and halo exchanges.
Nov 15, 2022	Multiple papers accepted in the Machine Learning and the Physical Sciences Workshop at NeurIPS 2022
Nov 7, 2022	New astro paper on Differentiable Halo Occupation Distributions led by Ben Horowitz demonstrating that it’s not because your model is stochastic and involves discrete variables that you can’t backprop through it

selected publications

Differentiable Stochastic Halo Occupation Distribution

Benjamin Horowitz, ChangHoon Hahn, Francois Lanusse, and 2 more authors

arXiv e-prints Nov 2022

Abs arXiv Bib Code

In this work, we demonstrate how differentiable stochastic sampling techniques developed in the context of deep Reinforcement Learning can be used to perform efficient parameter inference over stochastic, simulation-based, forward models. As a particular example, we focus on the problem of estimating parameters of Halo Occupancy Distribution (HOD) models which are used to connect galaxies with their dark matter halos. Using a combination of continuous relaxation and gradient parameterization techniques, we can obtain well-defined gradients with respect to HOD parameters through discrete galaxy catalogs realizations. Having access to these gradients allows us to leverage efficient sampling schemes, such as Hamiltonian Monte-Carlo, and greatly speed up parameter inference. We demonstrate our technique on a mock galaxy catalog generated from the Bolshoi simulation using the Zheng et al. 2007 HOD model and find near identical posteriors as standard Markov Chain Monte Carlo techniques with an increase of \raisebox-0.5ex\textasciitilde8x in convergence efficiency. Our differentiable HOD model also has broad applications in full forward model approaches to cosmic structure and cosmological analysis.
@article{2022arXiv221103852H, adsnote = {Provided by the SAO/NASA Astrophysics Data System}, adsurl = {https://ui.adsabs.harvard.edu/abs/2022arXiv221103852H}, archiveprefix = {arXiv}, author = {{Horowitz}, Benjamin and {Hahn}, ChangHoon and {Lanusse}, Francois and {Modi}, Chirag and {Ferraro}, Simone}, doi = {10.48550/arXiv.2211.03852}, eid = {arXiv:2211.03852}, eprint = {2211.03852}, journal = {arXiv e-prints}, keywords = {Astrophysics - Cosmology and Nongalactic Astrophysics, Astrophysics - Astrophysics of Galaxies}, month = nov, pages = {arXiv:2211.03852}, primaryclass = {astro-ph.CO}, title = {{Differentiable Stochastic Halo Occupation Distribution}}, year = {2022} }
Nat. Rev. Phys
Bayesian uncertainty quantification for machine-learned models in physics

Yarin Gal, Petros Koumoutsakos, Francois Lanusse, and 2 more authors

Nature Reviews Physics Aug 2022

Abs Bib

Being able to quantify uncertainty when comparing a theoretical or computational model to observations is critical to conducting a sound scientific investigation. With the rise of data-driven modelling, understanding various sources of uncertainty and developing methods to estimate them has gained renewed attention. Five researchers discuss uncertainty quantification in machine-learned models with an emphasis on issues relevant to physics problems.
@article{2022NatRP...4..573G, adsnote = {Provided by the SAO/NASA Astrophysics Data System}, adsurl = {https://ui.adsabs.harvard.edu/abs/2022NatRP...4..573G}, author = {{Gal}, Yarin and {Koumoutsakos}, Petros and {Lanusse}, Francois and {Louppe}, Gilles and {Papadimitriou}, Costas}, doi = {10.1038/s42254-022-00498-4}, journal = {Nature Reviews Physics}, month = aug, number = {9}, pages = {573-577}, title = {{Bayesian uncertainty quantification for machine-learned models in physics}}, volume = {4}, year = {2022} }