Laurence Perreault-Levasseur

tara.akhoundsadegh@mila.quebec

Biography

Laurence Perreault-Levasseur currently holds the Canada Research Chair in Computational Cosmology and Artificial Intelligence. She is an Associate Professor at the University of Montréal and a Core Member of Mila, where she conducts research in the development and application of machine learning methods to cosmology.

She is also a Visiting Scholar at the Flatiron Institute in New York City. Prior to that, she was a Flatiron research fellow at the Center for Computational Astrophysics in the Flatiron Institute and a KIPAC postdoctoral fellow at Stanford University. Laurence completed her PhD degree at the University of Cambridge, where she worked on applications of open effective field theory methods to the formalism of inflation. She received her B.Sc. and M.Sc. degrees from McGill University.

Current Students

Tara Akhound-Sadegh

PhD - McGill University

Principal supervisor :

Siamak Ravanbakhsh

Missael Barco

PhD - Université de Montréal

Co-supervisor :

missael-gabriel.barco@mila.quebec

Misha Barth

PhD - Université de Montréal

Principal supervisor :

michael.barth@mila.quebec

mohammed.chamma@mila.quebec

Mohammed Chamma

Postdoctorate - McGill University

PhD - Université de Montréal

Co-supervisor :

andreas.filipp@umontreal.ca

Ronan Legin

PhD - Université de Montréal

Principal supervisor :

ronan.legin@mila.quebec

michael.matesic@mila.quebec

Michael Matesic

PhD - Université de Montréal

guillaume.payeur@mail.mcgill.ca

Guillaume Payeur

PhD - Université de Montréal

Master's Research - Université de Montréal

Principal supervisor :

nicolas.payot@mila.quebec

Sacha Perry-Fagant

PhD - Université de Montréal

Co-supervisor :

sacha.perry-fagant@mila.quebec

sammy.sharief@mila.quebec

Salma Salhi

PhD - Université de Montréal

salhi.salma@mila.quebec

Ketan Sand

Postdoctorate - McGill University

ketan.sand@mila.quebec

Sammy Nasser Sharief

Master's Research - Université de Montréal

Postdoctorate - Université de Montréal

Principal supervisor :

nolan.smyth@mila.quebec

Yitian Sun

Postdoctorate - McGill University

Co-supervisor :

yitian.sun@mila.quebec

Aya Tessier

Master's Research - Université de Montréal

aya.astro.21@gmail.com

Justine Zeghal

Postdoctorate - Université de Montréal

Co-supervisor :

justine.zeghal@mila.quebec

Publications

Microcaustics: Fast GPU-Accelerated Dynamical Microlensing of Time-Dependent Extended Sources

Joshua Fagin

Connor Stone

James Hung-Hsu Chan

Sophia Miskiewicz

Henry Best

Paras Sharma

Matthew O’Dowd

Simon Birrer

Gravitational microlensing probes unresolved quasar accretion disc structure and compact objects in lens galaxies and is an important system… (see more)atic in strong-lens time-delay cosmography. Most analyses use static microlensing map simulations because evolving individual microlenses and caustics is computationally prohibitive. We present Microcaustics, a GPU-accelerated Python package built on PyTorch for dynamical microlensing of time-dependent extended sources in strong gravitational lenses. It combines a local-exact/far-field deflection solver, source-directed lens-plane scouting with temporal reuse, and biquadratic inverse polygon mapping (IPM). GPU batching and fused Triton rasterization accelerate the calculation, while binary caustic labels track source-centred crossings. Across three simulated quadruply lensed quasar systems, our package generates a 10-year, 147-frame multiband light curve in an amortized median of 0.68 s (0.88 s with labels) on a consumer GPU with a median light-curve RMSE of 0.35 mmag, while recovering every reference source-centred caustic crossing. At comparable map accuracy, the batched throughput is 780 times higher than that of a recent CUDA IPM implementation and extrapolates to a speedup of millions relative to classic GPU-D. We demonstrate that our IPM converges as

2026-10-13

arXiv (preprint)

PM++: Scaling Differentiable Cosmological Simulators with Dynamic Particle Ownership

Nicolas Payot

Large differentiable particle-mesh simulations support cosmological inference and simulator-coupled learning, but their evolving states can … (see more)exceed single-accelerator memory. Distribution with fixed particle ownership remains memory-limited because deposition halos must cover the maximum cumulative displacement. We present

2026-10-02

Sim2Sci @ Neural Information Processing Systems (published)

Microlensing Detection and Inference via Learned Bayes Factors

We present a unified framework for gravitational microlensing event detection and parameter inference. Traditional pipelines use determinist… (see more)ic hard cuts on photometric statistics, systematically missing low-magnification events in the finite-source regime. We instead frame detection as Bayesian model comparison using Evidence Networks, which learn calibrated Bayes factors from binary-labeled simulations, and combine this with Neural Posterior Estimation (NPE) for amortized parameter inference. Both share a transformer encoder that handles irregularly-sampled time series without imputation. On simulated Roman Space Telescope data, our Evidence Network achieves

2026-05-29

AI4Physics @ International Conference on Machine Learning (published)

Strong Gravitational Lensing Posterior Sampling in Pixel-Space Using Diffusion Models and Recurrent Inference Machines

Guillaume Payeur

Gabriel Missael Barco

Modeling galaxy-galaxy strong gravitational lenses to infer the brightness of the source galaxy and the mass distribution of the foreground … (see more)galaxy is computationally challenging, particularly for high-resolution, high signal-to-noise observations. In this regime, high-dimensional representations of both the source and the foreground mass distribution are necessary to model the data down to the noise level. This inference problem has been challenging for both traditional and machine learning-based methods because of its high dimensionality and its non-linearity in the foreground mass distribution. We present a method to generate joint posterior samples of the source galaxy and foreground mass distribution as pixelated images conditioned on observations. The method combines diffusion-based generative modeling and recurrent inference machines. It can model realistic gravitational lensing simulations with background and foreground galaxies drawn from cosmological hydrodynamical simulations down to the noise level.

2026-05-29

AI4Physics @ International Conference on Machine Learning (published)

MIRA: A Score for Conditional Distribution Accuracy and Model Comparison

Sammy Sharief

Justine Zeghal

Gabriel Missael Barco

Pablo Lemos

We present Mira, a method for estimating the expected probability that samples from a candidate conditional distribution match the true, unk… (see more)nown conditional distribution, for which only data-label pairs are available. We derive theoretical bounds obtained when the candidate distribution matches the true one and when the conditional distributions are independent. This framework thus enables model comparison by quantifying the alignment between the conditional distribution of a candidate model and the data-label pairs of the true model. Consequently, Mira enables Bayesian model comparison through direct posterior validation, bypassing the challenging evidence computation. We demonstrate its effectiveness across several toy problems and Bayesian inference tasks.

2026-04-29

International Conference on Machine Learning (spotlight)

proceedings.mlr.press

LSST Strong Lensing Systems Dark Matter Sensitivity Analysis with Neural Ratio Estimators

Andreas Filipp

Daniel Gilman

LSST Dark Energy Science Collaboration

Strong gravitational lensing offers a unique probe of dark matter (DM) on sub-galactic scales, where the abundance and distribution of low-m… (see more)ass halos are highly sensitive to the underlying properties of DM particles. In this work, we forecast LSST's sensitivity to DM substructure in galaxy-galaxy strong lenses using simulated samples and neural ratio estimators (NREs). Our simulations include both subhalos within the main deflector and line-of-sight (LOS) halos, with halo masses down to

2026-04-07

arXiv (preprint)

A Fast Generative Framework for High-dimensional Posterior Sampling: Application to CMB Delensing

Hadi Sotoudeh

Pablo Lemos

We introduce a deep generative framework for high-dimensional Bayesian inference that enables efficient posterior sampling. As telescopes an… (see more)d simulations rapidly expand the volume and resolution of astrophysical data, fast simulation-based inference methods are increasingly needed to extract scientific insights. While diffusion-based approaches offer high-quality generative capabilities, they are hindered by slow sampling speeds. Our method performs posterior sampling an order of magnitude faster than a diffusion baseline. Applied to the problem of CMB delensing, it successfully recovers the unlensed CMB power spectrum from simulated observations. The model also remains robust to shifts in cosmological parameters, demonstrating its potential for out-of-distribution generalization and application to observational cosmological data.

2026-03-03

arXiv (preprint)

Opportunities in AI/ML for the Rubin LSST Dark Energy Science Collaboration

LSST Dark Energy Science Collaboration

Eric Aubourg

Camille Avestruz

Matthew R. Becker

Biswajit Biswas

Rahul Biswas

Boris Bolliet

Adam S. Bolton

Clecio R. Bom

Raphaël Bonnet-Guerrini

Alexandre Boucaud

Jean-Eric Campagne

Chihway Chang

Aleksandra Ćiprijanović

Johann Cohen-Tanugi

Michael W. Coughlin

John Franklin Crenshaw

Juan C. Cuevas-Tello

Juan de Vicente

Seth W. Digel … (see 46 more)

Steven Dillmann

Mariano Javier de León Dominguez Romero

Alex Drlica-Wagner

Sydney Erickson

Alexander T. Gagliano

Christos Georgiou

Aritra Ghosh

Matthew Grayling

Kirill A. Grishin

Alan Heavens

Lindsay R. House

Mustapha Ishak

Wassim Kabalan

Arun Kannawadi

François Lanusse

C. Danielle Leonard

Pierre-François Léget

Michelle Lochner

Yao-Yuan Mao

Peter Melchior

Grant Merz

Martin Millon

Anais Möller

Gautham Narayan

Yuuki Omori

Hiranya Peiris

Andrés A. Plazas Malagón

Nesar Ramachandra

Benjamin Remy

Cécile Roucelle

Jaime Ruiz-Zapatero

Stefan Schuldt

Ignacio Sevilla-Noarbe

Ved G. Shah

Tjitske Starkenburg

Stephen Thorp

Laura Toribio San Cipriano

Tilman Tröster

Roberto Trotta

Padma Venkatraman

Amanda Wasserman

Tim White

Justine Zeghal

Tianqing Zhang

Yuanyuan Zhang

The Vera C. Rubin Observatory's Legacy Survey of Space and Time (LSST) will produce unprecedented volumes of heterogeneous astronomical data… (see more) (images, catalogs, and alerts) that challenge traditional analysis pipelines. The LSST Dark Energy Science Collaboration (DESC) aims to derive robust constraints on dark energy and dark matter from these data, requiring methods that are statistically powerful, scalable, and operationally reliable. Artificial intelligence and machine learning (AI/ML) are already embedded across DESC science workflows, from photometric redshifts and transient classification to weak lensing inference and cosmological simulations. Yet their utility for precision cosmology hinges on trustworthy uncertainty quantification, robustness to covariate shift and model misspecification, and reproducible integration within scientific pipelines. This white paper surveys the current landscape of AI/ML across DESC's primary cosmological probes and cross-cutting analyses, revealing that the same core methodologies and fundamental challenges recur across disparate science cases. Since progress on these cross-cutting challenges would benefit multiple probes simultaneously, we identify key methodological research priorities, including Bayesian inference at scale, physics-informed methods, validation frameworks, and active learning for discovery. With an eye on emerging techniques, we also explore the potential of the latest foundation model methodologies and LLM-driven agentic AI systems to reshape DESC workflows, provided their deployment is coupled with rigorous evaluation and governance. Finally, we discuss critical software, computing, data infrastructure, and human capital requirements for the successful deployment of these new methodologies, and consider associated risks and opportunities for broader coordination with external actors.

2026-01-19

Zenodo (published)

Caustics: A Python Package for Accelerated Strong Gravitational Lensing Simulations

Connor Stone

Alexandre Adam

Adam Coogan

M. J. Yantovski-Barth

Andreas Filipp

Landung Setiawan

Cordero Core

Ronan Legin

Charles Wilson

Gabriel Missael Barco

2025-12-31

Zenodo (published)

The 2nd Workshop on Foundation Models for Science: Real-World Impact and Science-First Design

Wuyang Chen

Yongji Wang

N. Benjamin Erichson

Bo Li

Damian Borth

Swarat Chaudhuri

Scientific foundation models should be built for science, not for generic AI tastes or leaderboard prestige. This workshop centers problem-d… (see more)riven design: models that measurably advance real scientific inquiries, e.g., forecasting extreme climate events, accelerating materials discovery, understanding biological mechanisms, co-developed with domain experts and validated against field data, experiments, and downstream impact. We argue that foundation models for science must be built differently from language and vision. Scientific data are physical, causal, spatiotemporal, and often scarce or biased; objectives must reflect mechanistic fidelity, not just predictive accuracy. This calls for scientific priors and constraints, robust uncertainty quantification (UQ), and architectures that natively handle multi-modality (e.g., grids, meshes, spectra, time series, point clouds, text, images, code). It also demands tight integration with classical scientific tools (simulators, PDE solvers, optimization and inference engines, and HPC workflows) to yield hybrid systems that are faster, more accurate, and more trustworthy. We will highlight opportunities and hard problems unique to science: enforcing conservation laws and symmetries; learning across vast spatial and temporal scales; representing extreme events and tipping points; calibrating and validating UQ; and developing evaluation protocols that reward mechanistic insight and actionable reliability. The goal is a roadmap for building, training, and deploying scientific foundation models that accelerate discovery while respecting the structure of the natural world.

2025-12-31

Workshop Proposals @ International Conference on Learning Representations (published)

Transformer Embeddings for Fast Microlensing Inference

2025-12-11

ArXiv (preprint)

Neural Deprojection of Galaxy Stellar Mass Profiles

M. J. Yantovski-Barth

Hengyue Zhang

Connor Stone

Martin Bureau

We introduce a neural approach to dynamical modeling of galaxies that replaces traditional imaging-based deprojections with a differentiable… (see more) mapping. Specifically, we train a neural network to translate Nuker profile parameters into analytically deprojectable Multi Gaussian Expansion components, enabling physically realistic stellar mass models without requiring optical observations. We integrate this model into SuperMAGE, a differentiable dynamical modelling pipeline for Bayesian inference of supermassive black hole masses. Applied to ALMA data, our approach finds results consistent with state-of-the-art models while extending applicability to dust-obscured and active galaxies where optical data analysis is challenging.

2025-11-24

arXiv (preprint)