PhD student
Institut Jean Nicod, ENS-PSL & Chargée d’études CNRS
aidaelamrani [at] outlook.fr
Master in Theoretical Computer Science / Aix-Marseille Université
Information in the Interplay Between Mind and Matter.
PhD student
Institute Curie
loic.chadoutaud [at] curie.fr
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)
Spatial and Temporal Heterogeneity of single cell transcriptomic data.
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.
PhD student
Sorbonne University
guanghui.fu [at] icm-institute.org
Master of Software Engineering; Beijing University of Technology
Segmentation, classification and generative models for computer-aided diagnosis of neurological diseases from neuroimaging data.
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.
PhD student
ENS
samaran [at] bio.ens.psl.eu
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)
Methods for single-cell multimodal integration.
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.
Master degree, National Engineering School of Sousse, Tunisia
Engineer degree, National Engineering School of Sousse, Tunisia
Stable feature selection in multi-locus Genome Wide Association Studies.
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.
PhD student
École normale supérieure - PSL
salome.do [at] ens.psl.eu
MSc / Engineering degree at ENSAE IP Paris
Computational Content Analysis Methods for News Frames Prevalence Estimation in the Political Press.
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.
Master’s degree in Theoretical Physics, 2021, Technical University Berlin
Analysing discourse and semantics through geometric representations.
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.
PhD Student
Université de Paris / Inria / Inserm / Implicity (CIFRE thesis)
louis.vincent [at] implicity.fr
Master 2 – Mathématiques, Vision & Apprentissage (ENS Paris-Saclay),
Master 2 – Statistiques (Sorbonne Université – Campus Pierre et Marie Curie)
Longitudinal data encoding applied to medical decision support in telecardiology.
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.
Master 2 IASD (ENS Ulm)
Context-dependent collective decisions.
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.
PhD Student
Université Dauphine-PSL
jerome.arjonilla [at] dauphine.psl.eu
Master in Computer Science, Sorbonne University + Double Bachelor in Mathematics and Economics, Université Toulouse
Search and Learning algorithm for games with imperfect information.
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).
