Short bio

• Diplôme de l’Ecole polytechnique
• MSc of Computational and Mathematical Engineering – Stanford University

Thesis title

Study of causal networks.

Short abstract

Causal inference is very important to a wide range of use from clinical trials to econometrics: we learned that “correlation is not causation” but how can we learn true causal relationships? We will using learning of causal diagrams using the latest advances in optimization. We will also study experimental design, given your current knowledge, how to best use your limited resources to gain insightful causal information (e.g., by doing biological experimentations)?

Short bio

Master 2 MVA , ENS Paris-Saclay

Thesis title

Apprentissage non-supervisé de représentations et applications en santé (Unsupervised representation learning and applications in healthcare).

Short abstract

Unsupervised representation learning has recently caught up with the performance of supervised approaches thanks to the introduction of contrastive methods. The mathematical study of these new methods is essential to better understand and exploit them as well as provide guarantees on their performance. Their application is especially relevant in healthcare where supervision is commonly lacking.

Short bio

Master Degree “Physics of Complex Systems” at Université de Paris

Thesis title

Knit-quakes Prediction through Machine Learning.

Short abstract

Knitted fabric is a thread-based metamaterial that exhibits crackling noise both in the force signal and in the deformation field when deformed. The occurrence of those intermittent, scale-invariant events cannot be accurately predicted at the time being, as in most systems with similar avalanche-like behavior. However, Machine Learning methods have proven to be a useful tool when dealing with time-series predictions and image processing, giving hope to be able to predict relevant knit-quakes quantities.

Short bio

Master of Science in Public Health, Comparative Effectiveness Research (University of Paris)

Thesis title

Estimation of the Individual Treatment Effect via Machine Learning using an Individual Participant Meta-Analysis.

Short abstract

Personalized medicine aims at tailoring a treatment to the individual characteristics of each patient. One key aspect of personalized medicine is to identify the subgroups of patients who benefit from an intervention, which we can do by estimating the Individual Treatment Effect (ITE). My goal is to develop Machine Learning methods to estimate the ITE using Individual Participant Meta-Analyses with binary and time-to-event outcomes. I will develop these methods on RCTs and observational data. I will also use federated learning to estimate the ITE.

Short bio

Graduated from Ecole Polytechnique in 2019

Master’s degree at Université Paris-Sud

Research domain

Machine Learning and Optimization

Thesis title

Statistical inference on graphs: the graph alignment problem.

Short abstract

We study inference problems in graphs and matrices, such as graph alignment, which aims at finding a matching between nodes of two graphs preserving most of the edges. In a Bayesian setting, several approaches may be followed: analyzing rigorously existing algorithms based on random models to determine the regimes in which they may succeed, look at the information-theoretical and computational thresholds, or design and propose new algorithms that explore new regimes.

Short bio

• Master 2 – Mathématiques, Vision & Apprentissage – ENS Paris-Saclay
• Ingénieur Civil des Mines de Paris  – Mines ParisTech

Thesis title

Exploration of the latent space modelling of Variational Autoencoders.

Short abstract

In the medical field, the lack of data resulting in low patient variability remains a key issue. For example, in neuroscience, practitioners have to deal with potentially very high-dimensional data combined with a very small number of samples. Generative models such as variational autoencoders (VAE) may reveal particularly well suited to perform dimension reduction on such data and their generative capacity could be used for data augmentation. Unfortunately, VAEs perform weakly when trained on a (very) small number of data and the underlying structure of the latent space remains poorly understood. We believe that further investigating the latent space geometry of these generative models could allow us to overcome these issues.