- Hongliang Lü (from 2017) co-advised with Florence Forbes (Inria)
- Marta Crispino (from March 2018) co-advised with Stéphane Girard (Inria)
- Thèse Cifre opportunity around Constrained machine learning using deep neural networks for embedded systems. Contact me if interested.
- Mariia Vladimirova, Université Grenoble-Alpes, co-advised with Jakob Verbeek (Inria). Prior specification for Bayesian deep learning models and regularization implications.
- Fabien Boux (from 2017) co-advised with Florence Forbes (Inria) and Emmanuel Barbier (GIN)
- Verónica Muñoz Ramírez (from 2017) co-advised with Florence Forbes (Inria) and Michel Dojat (Inserm, GIN)
2018 - Caroline Lawless, Trinity College Dublin. An elementary derivation of the Chinese restaurant process from the stick-breaking representation for the Pitman--Yor process.
2018 - Mariia Vladimirova, Université Grenoble-Alpes, co-advised with Pablo Mesejo (Inria). Wide limit of deep Bayesian neural networks.
2018 - Aleksandra Malkova, Université Grenoble-Alpes, co-advised with Maria Laura Delle Monache. DATASAFE: understanding Data Accidents for TrAffic SAFEty.
2018 - Michal Lewandowski, Université Grenoble-Alpes. Theoretical properties of Bayesian nonparametric clustering.
2016 - Cecilia Ferrando, Collegio Carlo Alberto, Moncalieri, Italy. Bayesian stochastic blockmodels.