Claire Donnat

Claire Donnat
École Polytechnique

Doctor of Philosophy

About

23
Publications
7,676
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743
Citations

Publications

Publications (23)
Preprint
Full-text available
This paper presents the computational challenge on differential geometry and topology that was hosted within the ICLR 2022 workshop ``Geometric and Topological Representation Learning". The competition asked participants to provide implementations of machine learning algorithms on manifolds that would respect the API of the open-source software Geo...
Preprint
Full-text available
Recent breakthroughs in high resolution imaging of biomolecules in solution with cryo-electron microscopy (cryo-EM) have unlocked new doors for the reconstruction of molecular volumes, thereby promising further advances in biology, chemistry, and pharmacological research amongst others. Despite significant headway, the immense challenges in cryo-EM...
Preprint
Full-text available
Pooled testing offers an efficient solution to the unprecedented testing demands of the COVID-19 pandemic, although with potentially lower sensitivity and increased costs to implementation in some settings. Assessments of this trade-off typically assume pooled specimens are independent and identically distributed. Yet, in the context of COVID-19, t...
Article
The correct evaluation of the reproductive number R for COVID-19 is central in the quantification of the potential scope of the pandemic and the selection of an appropriate course of action. In most models, R is modeled as a constant - effectively averaging out the inherent variability of the transmission process due to varying individual contact r...
Article
Full-text available
Background: Modelling COVID-19 transmission at live events and public gatherings is essential to control the probability of subsequent outbreaks and communicate to participants their personalised risk. Yet, despite the fast-growing body of literature on COVID transmission dynamics, current risk models either neglect contextual information on vacci...
Preprint
Full-text available
Modelling COVID-19 transmission at live events and public gatherings is essential to evaluate and control the probability of subsequent outbreaks. Model estimates can be used to inform event organizers about the possibility of super-spreading and the predicted efficacy of safety protocols, as well as to communicate to participants their personalise...
Preprint
Computer-Aided Diagnosis has shown stellar performance in providing accurate medical diagnoses across multiple testing modalities (medical images, electrophysiological signals, etc.). While this field has typically focused on fully harvesting the signal provided by a single (and generally extremely reliable) modality, fewer efforts have utilized im...
Article
Full-text available
Data analysis workflows in many scientific domains have become increasingly complex and flexible. Here we assess the effect of this flexibility on the results of functional magnetic resonance imaging by asking 70 independent teams to analyse the same dataset, testing the same 9 ex-ante hypotheses1. The flexibility of analytical approaches is exempl...
Preprint
The current COVID-19 pandemic is leading experts to assess the risks posed by the disease and compare policies geared towards stalling its evolution as a global pandemic. In this setting, the virus' basic reproductive number R_0, which characterizes the average number of secondary cases generated by each primary case, takes on a significant importa...
Preprint
Full-text available
We introduce Geomstats, an open-source Python toolbox for computations and statistics on nonlinear manifolds, such as hyperbolic spaces, spaces of symmetric positive definite matrices, Lie groups of transformations, and many more. We provide object-oriented and extensively unit-tested implementations. Among others, manifolds come equipped with fami...
Preprint
Full-text available
Data analysis workflows in many scientific domains have become increasingly complex and flexible. To assess the impact of this flexibility on functional magnetic resonance imaging (fMRI) results, the same dataset was independently analyzed by 70 teams, testing nine ex-ante hypotheses. The flexibility of analytic approaches is exemplified by the fac...
Preprint
Full-text available
Brain connectomics is a developing field in neurosciences which strives to understand cognitive processes and psychiatric diseases through the analysis of interactions between brain regions. However, in the high-dimensional, low-sample, and noisy regimes that typically characterize fMRI data, the recovery of such interactions remains an ongoing cha...
Preprint
Full-text available
Convex clustering is a recent stable alternative to hierarchical clustering. It formulates the recovery of progressively coalescing clusters as a regularized convex problem. While convex clustering was originally designed for handling Euclidean distances between data points, in a growing number of applications, the data is directly characterized by...
Conference Paper
Nodes residing in different parts of a graph can have similar structural roles within their local network topology. The identification of such roles provides key insight into the organization of networks and can be used for a variety of machine learning tasks. However, learning structural representations of nodes is a challenging problem, and it ha...
Article
From longitudinal biomedical studies to social networks, graphs have emerged as essential objects for describing evolving interactions between agents in complex systems. In such studies, after pre-processing, the data are encoded by a set of graphs, each representing a system’s state at a different point in time or space. The analysis of the system...
Preprint
Full-text available
We introduce geomstats, a python package that performs computations on manifolds such as hyperspheres, hyperbolic spaces, spaces of symmetric positive definite matrices and Lie groups of transformations. We provide efficient and extensively unit-tested implementations of these manifolds, together with useful Riemannian metrics and associated Expone...
Article
Full-text available
From longitudinal biomedical studies to social networks, graphs have emerged as a powerful framework for describing evolving interactions between agents in complex systems. In such studies, the data typically consists of a set of graphs representing a system's state at different points in time or space. The analysis of the system's dynamics depends...
Article
Full-text available
Nodes residing in different parts of a graph can have similar structural roles within their local network topology. The identification of such roles provides key insight into the organization of networks and can also be used to inform machine learning on graphs. However, learning structural representations of nodes is a challenging unsupervised-lea...

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