Short bio

• Diplôme d’etude supérieur de Santé Publique, Université Paris Descartes, 2020
• Double-cursus Médecine-sciences, 2016, Université Paris Descartes
• Master 2 de science-cognitives, 2013, École normale supérieure, Paris

Thesis title

Predictive modelling using structure learning to handle distribution shift in Electronic Health Records.

Short abstract

I develop machine learning models tailored to Electronic Health Records, which are multiple multivariate time series, with the particularity of being of mixed type and irregularly sampled. These models are generalized Gaussian Process models, and I write and evaluate kernels that encodes structural assumptions on the data generating process.

Short bio

Master MASH – Université Paris-Dauphine

Thesis title

ABC methods for PDE

Short bio

• Master, Sorbonne University
• Engineer, Mines Paristech

Research project

Modelling changes of dynamic over longitudinal data.

Short abstract

The objective is to develop models for longitudinal data and events in the context of neurodegenerative diseases. The models could then be used for prediction and/or for the description of the pathology.

Short bio

Ingénieur polytechnicien, École polytechnique (Palaiseau)

MPhil in Computational biology, University of Cambridge (UK)

Thesis title

Combine artificial intelligence with high resolution microscopy to better dissect the mechanism of binding and mechanism of action of multi-specific biologics.

Short abstract

Photo-activated localization microscopy (PALM) enables high-resolution recording of single proteins trajectories in live cells, thus providing precious probes of small-scale properties of the protein environment. I use graph neural networks to characterize relevant physical properties of these dynamics, and developed a flexible analysis scheme able to deal both with the diversity of motion types encountered in nature and with the fact that observed trajectories inevitably differ, to some extent, from archetypal theoretic models.

Short bio

Diplome d’ingénieur – ENSAE Paris

Master Statistics And Machine Learning, Paris Saclay University

Thesis title

Improvement of MCMC methods and adaptation to the Big Data.

Short abstract

MCMC methods can have some difficulties exploring space, especially in high dimensional settings that can occur in a context of Big Data. The goal of my PhD thesis is to find enhancements to MCMC about this exploring issue.

Short bio

• MSc in Statistical Science, Oxford University
• Double licence M.I.A.S.H.S, Université Paris 1 Panthéon-Sorbonne & SciencesPo Paris

Thesis title

Foundations and applications in Bayesian Mixture Modelling.

Short abstract

Mixtures are a popular class of models bridging parametric and non-parametric statistics and, as part of the standard data analysis toolkit, have ubiquitous applications in regression, clustering, machine learning, etc. One of the main goals of this thesis is to ease model selection within such a class of models, in particular by finding efficient ways of computing the marginal likelihood (aka evidence) of semi-parametric models (such as Dirichlet Process Mixtures). We also study the convergence properties of the Bayes Factor when comparing such parametric and semi-parametric models.

Short bio

• MS in Probabilities and Finance (ex DEA El Karoui) from UPMC
• BSc in Mathematics from École Normale Supérieure of Rennes

Thesis title

Modelling of multiple time series with learning of the structure across series.

Short abstract

Many phenomena observed in nature can be described as a collection of time series (component of an audio recording, pixels of a video over time, economic agents of a complex system). The goal of this PhD is to model multiple time series and to learn the structure across series.

Short bio

M2 Bioinformatics & Modelling, Sorbonne Université

Thesis title

Integration of multi-level single cell molecular data to unravel the mechanisms of oncogene activation effect on cellular phenotypes.

Short abstract

We investigate oncogene effects on cancer cells driving the shift from wild-type to malignant phenotype using single-cell data. Getting rid of patient-specific information among several cancer datasets is a crucial consideration to answer this question, as it blurs tumoral signal of interest. Furthermore, developing methods allowing for information integration between several data acquisition techniques (RNA-seq, ATAC-seq…) may yield very insightful and relevant results for investigating systems biology of cancers.