Elamrani Aïda

PhD student

Institut Jean Nicod, ENS-PSL & Chargée d’études CNRS

aidaelamrani [at] outlook.fr

Short bio

Master in Theoretical Computer Science / Aix-Marseille Université

Thesis topic

Information in the Interplay Between Mind and Matter.

Short abstract

Chadoutaud Loïc

PhD student

Institute Curie

loic.chadoutaud [at] curie.fr

Short bio

Ingénieur Civil des Mines de Paris  – Mines ParisTech (Master’s degree in Science and Executive Engineering)

Master 2 – Mathématiques, Vision & Apprentissage – ENS Paris-Saclay (MVA Master’s degree)

Thesis topic

Spatial and Temporal Heterogeneity of single cell transcriptomic data.

Short abstract

Spatial transcriptomics is a new kind of technology that allows biologists to measure both transcriptomic information and spatial locations in tissues. My project aims to develop methods (such as clustering, dimensionality reduction algorithm…) for the analysis of such multimodal data. In particular, we are mainly interested in the links between spatial organization and transcriptomic heterogeneity within tissues.

FU Guanghui

PhD student

Sorbonne University

guanghui.fu [at] icm-institute.org

Short bio

Master of Software Engineering; Beijing University of Technology

Thesis topic

Segmentation, classification and generative models for computer-aided diagnosis of neurological diseases from neuroimaging data.

Short abstract

The objective of this project is to design and validate deep learning methods for computer-assisted diagnosis of neurological disorders, and in particular methods that can deal with applications where annotated data is limited.

SAMARAN Jules

PhD student

ENS

samaran [at] bio.ens.psl.eu

Short bio

Ingénieur Civil des Mines de Paris  – Mines ParisTech (Master’s degree in Science and Executive Engineering)

Master 2 – Mathématiques, Vision & Apprentissage – ENS Paris-Saclay (MVA Master’s degree)

Thesis topic

Methods for single-cell multimodal integration.

Short abstract

Recent technological advances allow biologists to profile multiple modalities (e.g. gene expression, DNA methylation, chromatin accessibility, etc.) from a single cell. However, such data are still rare and most of the existing single-cell multi-modal data are profiled from different cells (i.e. unpaired data). My project aims at developing integrative dimensionality reduction approaches for unpaired multimodal data (i.e. a collection of monomodal data sets) that are adapted to single-cell data. This tool will enable to cluster cells based on their multimodal similarities, to extract markers from the different modalities and to transfer annotations from one data set to another.

NOUIRA Asma

PhD student

Mines ParisTech

asma.nouira [at] mines-paristech.fr

Short bio

Master degree, National Engineering School of Sousse, Tunisia

Engineer degree, National Engineering School of Sousse, Tunisia

Thesis topic

Stable feature selection in multi-locus Genome Wide Association Studies.

Short abstract

Our main goal is to provide a stable framework in Genome Wide Association Studies using Machine Learning, essentially feature selection models to deal with high-dimensional data. Many challenges lay ahead such as: genetic population stratification, linkage disequilibrium patterns clustering, the stability of the selection and the computational complexity. We aim to solve these issues by developing efficient algorithms applied to real data in case control studies such as  breast cancer disease.

DO Salomé

PhD student

École normale supérieure - PSL

salome.do [at] ens.psl.eu

Short bio

MSc / Engineering degree at ENSAE IP Paris

Thesis topic

Computational Content Analysis Methods for News Frames Prevalence Estimation in the Political Press.

Short abstract

This dissertation aims at providing Computational Content Analysis (CCA) methods for the analysis of News Framing in the political press. First, it aims at creating a french corpus of political press articles and providing human annotations for two news frames identification tasks, derived from the literature on strategic news framing and “horse race” journalism. Second, it aims at exploring the modalities (frame complexity, data quantity and data quality) in which Supervised Machine Learning (SML) methods can “augment” social scientists, i.e. train a model to generalize social scientists’ content analysis (CA) codebook (and subsequent text annotations) so that billions of articles can be analyzed instead of a few hundred. Third, the dissertation aims at evaluating the potential benefits of CCA over CA when it comes to estimating news frames prevalences in a corpus. What justifies using CCA over CA, and is it always justified? I will try to define the conditions on SML models performances under which news frames prevalence estimates are more accurate with CCA than CA.

POURNAKI Armin

PhD Student

ENS & PSL

pournaki [at] mis.mpg.de

Short bio

Master’s degree in Theoretical Physics, 2021, Technical University Berlin

Thesis title

Analysing discourse and semantics through geometric representations.

Short abstract

I explore geometric approaches to language and discourse analysis. Currently, I work on combining methods from network science and natural language processing to gain insights on the mechanisms behind information and knowledge spreading related to climate change.

VINCENT Louis

PhD Student

Université de Paris / Inria / Inserm / Implicity (CIFRE thesis)

louis.vincent [at] implicity.fr

Short bio

Master 2 – Mathématiques, Vision & Apprentissage (ENS Paris-Saclay),
Master 2 – Statistiques (Sorbonne Université – Campus Pierre et Marie Curie)

Thesis title

Longitudinal data encoding applied to medical decision support in telecardiology.

Short abstract

In telecardiology as in many other fields of modern medicine, we have at our disposal large amounts of data explaining the evolution of a patient. These data can often be missing or corrupted, and data from several sources can sometimes be of different nature, which makes their exploitation difficult.
My goal is to develop a model capable of synthesizing different types of temporal data via auto-encoders to infer the state of a patient. In the context of tele-cardiology, this could for instance allow us to predict deteriorations of a patient’s health status, and thus anticipate and prevent more serious complications.

DELEMAZURE Théo

PhD Student

Université Paris Dauphine-PSL

theo.delemazure [at] dauphine.eu

Short bio

Master 2 IASD (ENS Ulm)

Thesis title

Context-dependent collective decisions.

Short abstract

Traditional research in social choice (axiomatic or computational) consists in proposing collective decision mechanisms that are often too rigid. We will focus on the development of families of parameterized rules, which have enough variability and flexibility to allow the development of “tailor-made” mechanisms for specific problems.

ARJONILLA Jérôme

PhD Student

Université Dauphine-PSL

jerome.arjonilla [at] dauphine.psl.eu

Short bio

Master in Computer Science, Sorbonne University + Double Bachelor in Mathematics and Economics, Université Toulouse

Thesis title

Search and Learning algorithm for games with imperfect information.

Short abstract

Monte Carlo research has revolutionised game solving, and, combined with deep reinforcement learning has led to the creation of programs such as AlphaGo, Alpha Zero or Polygames that have beaten the best human players in many games. My thesis topic will focus on the extension of the methods of Monte Carlo and Deep Reinforcement Learning for imperfect information games with several players (multi-agent system).