Portrait de Noah El Rimawi-Fine n'est pas disponible

Noah El Rimawi-Fine

noah.elrimawi-fine@mila.quebec
Maîtrise recherche - McGill
Superviseur⋅e principal⋅e
Mathieu Blanchette
Co-supervisor
Siamak Ravanbakhsh
Sujets de recherche
Apprentissage profond
Biologie computationnelle
Modèles génératifs
GitHub

Publications

Simulation-free Structure Learning for Stochastic Population Dynamics
Noah El Rimawi-Fine
Adam Stecklov
Lucas Nelson
Alexander Tong
Mathieu Blanchette
Stephen Y. Zhang
Lazar Atanackovic
Modeling dynamical systems and unraveling their underlying structural dependencies is central to many domains in the natural sciences. Vario… (voir plus)us physical systems, such as those arising in cell biology, are inherently high-dimensional and stochastic in nature, and admit only partial, noisy state measurements. Our primary motivating setting is single-cell biology, where destructive measurements yield unpaired population snapshots rather than longitudinal trajectories of the same cells. This poses a significant challenge for addressing the problems of modeling the underlying dynamics and inferring the network structure of these systems. Existing methods are typically tailored either for structure learning or modeling dynamics at the population level, but are limited in their ability to address both problems together. In this work, we address both problems simultaneously: we present StructureFlow, a novel and principled simulation-free training approach for jointly learning the structure and stochastic population dynamics of physical systems. We showcase the utility of StructureFlow for the tasks of structure learning from interventions and dynamical (trajectory) inference of conditional population dynamics. We empirically evaluate our approach on high-dimensional synthetic systems, a set of biologically plausible simulated systems, and an experimental single-cell dataset. We show that StructureFlow can learn the structure of underlying systems while simultaneously modeling their conditional population dynamics --- a key step toward model-based mechanistic understanding of systems behavior.
2026-08-30
Transactions on Machine Learning Research (accepté)
openreview.net
openreview.net