Short bio

• BSc in Biology from the Lebanese American University 2017-2020
• MSc in Neuroscience from the American University of Beirut 2020-2022
• MSc in Learning Sciences from Université Paris Cité – Learning Planet Institute 2022-2024

Thesis Title

Investigating the Neural and Behavioral Mechanisms of Social Interaction to Better Understand Collaboration Among Peers.

Short Abstract

My research project investigates the neural and behavioral mechanisms underlying peer collaboration in middle childhood, focusing on inter-brain synchrony (IBS) as a key marker of effective social interaction. Using functional near-infrared spectroscopy (fNIRS) hyperscanning, we examine how IBS correlates with conversational behaviors and rapport-building during real-world collaborative tasks. Findings will inform the development of AI-driven virtual peers to enhance educational outcomes by fostering productive collaboration and social bonding among children.

Short bio

Msc in Engineering at ENSAE Paris

Thesis title

Exploration of multilingual and multimodal word embeddings.

Short abstract

The aim of this PhD is to study the advantage of visual information for the alignment of word embeddings in several languages, in particular for low-resource languages. Such multimodal embeddings may be particularly useful in scenarios where the text itself is partial or degraded, and where additional context (in the form of video data) could therefore be beneficial.

Short bio

• Master 2 – Informatique biomédicale, Sorbonne Université, Paris
• Docteur en cardiologie – Université Paris Cité

Thesis title

Deep representation of the patient’s electronic health record for clinical event prediction and patient similarity.

Short abstract

Pediatric cardiologist at Necker-Enfants Malades in Paris interested in health data extraction and reuse for clinical research. Her PhD project started in October 2020 supervised by Pr. Anita Burgun and co-supervised by Dr. Antoine Neuraz. Its objective is to propose a deep learning model to provide a reliable representation of the patient electronic health record.

Short bio

• Bachelor of Science in Mathematics and Computer Science, Université Grenoble Alpes
• Master of Engineering in Mathematics and Computer Science, École Centrale de Nantes

Thesis topic

Robust Neural Machine Translation.

Short abstract

Neural machine translation models struggle to translate texts that differ from the “standard” data commonly used to train them. In particular, social media texts pose many challenges because they tend to be “noisy”: non-standard use of spelling, grammar and vocabulary; typographical errors; use of emojis, hashtags and at-mentions; etc. I aim to develop new methods to better translate these texts.

Short bio

Master’s degree : mathematics (Université Paris – Dauphine) and social sciences (Sciences Po Paris)

Thesis topic

Using NLP to leverage social media data in the study of rare diseases.

Short abstract

My PhD thesis aims at exploring NLP techniques to study the online contents from rare diseases’ patients or their caregivers.  The first goal is to better understand natural histories of the studied diseases, and compare the spontaneously reported symptoms to symptoms collected during medical interviews. The other goal is to study how patients’ associations become invested in public policy governance, in particular by acquiring a vast knowledge collectively.

Short bio

Masters of Engineering, Ecole des Ponts

Thesis topic

Cheap and expressive neural contextual representations for textual data.

Short abstract

Neural language models are pre-trained using self-supervised learning to produce contextual representations of text data like words or sentences. These representations are shaped by the pre-training procedure: its data, its task, its optimization scheme, among others. I aim at identifying ways to improve the quality of the text representations by leveraging new pre-training approaches, in order to reduce data and/or compute requirements without quality loss.

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.

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.

Short bio

Engineer’s degree at Ecole CentraleSupélec (former Ecole Centrale Paris)

Master’s degree in Cognitive Science at the Ecole Normale Supérieure

Thesis title

Linguistic generalization in transformer-based neural language models.

Short abstract

Transformer-based neural architectures bear lots of promises as they seem to address a wide range of linguistic tasks after learning a language model. However, the level of abstraction they reach after their training is still opaque. My main research focus is understanding better how neural language models generalize. What linguistic properties do these architectures acquire during learning ? How is linguistic information encoded in their intermediate representation spaces?