Academic Team

Thomas BONALD is a professor at Télécom Paris and head of the DIG team (Data, Intelligence & Graphs). He holds a PhD degree from Ecole Polytechnique in Applied Mathematics. His main research interests are in machine learning for graphs, and its application to NLP and knowledge bases. He is one of the main contributors of scikit-network, a Python package for the analysis of large graphs.

Keywords: Graphs, machine learning, NLP, knowledge bases

Check his website

Jean-Louis Dessalles is associate professor at Télécom Paris in the LTCI laboratory and a member of the DIG research team. He uses artificial intelligence to model and simulate the relevance that characterizes spontaneous human communication. For a decade, he has been developing the Theory of Simplicity, which serves as a basis for modeling narrative interest and argumentative relevance. He also works on the simulation of evolutionary processes and on the fundamental question of the origin of human language, considered as a social signalling game. He is the author of several books, including Why We Talk (Oxford University Press. 2007), La pertinence et ses origines cognitives (Hermes), Le fil de la vie (Odile Jacob, 2016), Des intelligences TRÈS artificielles (Odile Jacob, 2019).

Keywords: Artifical intelligence

Check his website

Matthieu Labeau joined Télécom Paris as a Senior Lecturer in 2019. He completed his doctorate at the University of Paris-Saclay, and became interested in the problems posed by large vocabularies in language modeling. He then became a postdoctoral fellow at the University of Edinburgh. His research areas, within automatic language processing, mainly concern representation learning and language modeling. Keywords: Automatic language processing, representation learning, language modeling. Check his website

Pirmin LEMBERGER is scientific director at onepoint. He supervises the team of PhD students and interns in the TALia laboratory. He holds a PhD in theoretical physics from the Swiss Federal Institute of Technology in Lausanne (EPFL). His publications in this field concern correlated fermion systems and the application of quantum field theory techniques to statistical physics. After several years as an IT expert at SQLI (consulting company in Paris), he joined the data.ia community at onepoint (digital transformation architects) as a data science team leader. For the past few years, he has been particularly interested in deep learning, graph neural networks and NLP from an applied mathematics perspective. He is the lead author of the book Big Data et Machine Learning (Dunod 2019) and co-author of the book Le traitement automatique des langues (Dunod 2020). He is a contributor to the Towards Data Science blog.

Keywords: Deep learning, graph neural networks, NLP

Check his website

Bérengère MATHIEU  is a data scientist at onepoint. She works as a lead on industrial projects with an artificial intelligence components. She holds a PhD in image, information and hypermedia. Her publications are in the field of computer vision and concern semantic segmentation and over-segmentation techniques. After several years at Makina Corpus as a scientific and technical expert on machine learning and deep learning, she joined onepoint in 2022. Her work in research and development has gradually led her to natural language processing, particularly in the areas of classification, search and analysis of visually rich documents.

Keywords : vision, semantic segmentation, deep learning, search

Check her website

Antoine SAILLENFEST is graduated as an engineer at ENSTA Paris in 2011 (speciality AI and embedded systems), also holder of a Master in cognitive sciences from EHESS in 2011 and Doctor from Télécom Paris 2015 (speciality Computer Science and networks - thesis on cognitive modeling of narrative interest for story generation). He then did 4 years of mobile and web project management. In 2021 he joined the TALia laboratory as a researcher. His research focuses on the application of deep learning techniques for NLP with a specialization in data with graph structure.

Keywords: Deep learning, graphes, TALN, Intelligence Artificielle

Check his Github | Check his LinkedIn | Check his Google Scholar

Nathanaël BEAU is in his second year of PhD at the Laboratoire de Linguistique Formelle at Paris Cité University under the supervision of Benoît Crabbé. After obtaining his engineering degree at Arts et Métiers, he started his thesis on the subject of generating Python code from a natural language description. He approaches this question as a translation problem and is particularly interested in model optimization in low-ressources environment. He participated in the international conference Association for Computational Linguistics 2022 that took place in Dublin in May.

Keywords: Natural Language Processing, Code Generation, Neural Machine Translation, Transformer, Low-Ressources environment

Check his website

After graduating from Télécom Paris and Master MVA (ENS Paris-Saclay), Jean VASSOYAN joined onepoint and the Centre Borelli in 2021 for a PhD in machine learning applied to adaptive learning. More precisely, he leverages reinforcement learning algorithms and graph neural networks to build a model capable of recommending personalized learning paths.

Keywords: Adaptive learning, reinforcement learning, graph neural networks, learning path, recommendation engine, knowledge representation

Check his website