About

Some additional informations about this course.

Florence d’Alché-Buc is a full professor at Institut Polytechnique de Paris, Télécom Pari in Statistical Learning. She is the holder of the “Data Science and Artificial Intelligence for Digitalized Industry and Services” chair at Télécom Paris, and is mainly interested in learning from complex data, in particular the supervised prediction of graphs, functions and spatio-temporal signals, exploiting the structure and geometry of these objects in regularization and cost functions. More recently, she has been working on the interpretability and robustness of predictive models. Link to personal webpage.

Quentin Bouniot is a Post-Doctoral researcher at Institut Polytechnique de Paris, Télécom Paris. His current research topic focus on Uncertainty and Explainability in Deep Learning models. The topic of his PhD was on Few-shot Learning and Meta-Learning more specifically applied to Object Detection and Image Segmentation. Link to personal webpage.

Stephan Clemençon is a full professor at Institut Polytechnique de Paris, Télécom Paris. Link to personal webpage.

Yann Issartel is an assistant professor at Institut Polytechnique de Paris, Télécom Paris. His research lies in mathematical statistics and learning, especially in high-dimensional statistics, network analysis and data privacy. Link to personal webpage.

Charlotte Laclau is an assistant professor at Institut Polytechnique de Paris, Télécom Paris. Her current research works focus on representation learning for complex data (e.g. graph, text) with a specific interest on fairness in this particular context. Link to personal webpage.

Enzo Tartaglione is an assistant professor at Institut Polytechnique de Paris, Telecom Paris and an Hi!Paris chair holder. His principal interests include compression, sparsification, pruning and watermarking of deep neural networks, deep learning for medical imaging, privacy-aware learning, data debiasing, regularization for deep learning and neural networks growing. His expertise mainly focuses on the themes of efficient deep learning. Link to personal webpage.