Team
For further info about Statify team members, please visit the members' page.
Postdoc
Kostas Pitas (2022-2024), Inria. PAC-Bayesian generalization bounds.
Trung Tin Nguyen (2022-2023), Inria, with Florence Forbes and Hien Nguyen. Approximate Bayesian Computation.
Pierre Wolinski (2020-2023), University of Oxford & Inria, with Judith Rousseau. Bayesian deep learning.
PhD
Julien Zhou (2022-) Inria-Criteo, co-advised with Pierre Gaillard and Thibaud Rahier.
Louise Alamichel (2021-) Inria, co-advised with Guillaume Kon Kam King. Bayesian Nonparametric methods for complex genomic data.
Théo Moins (2020-), Inria, co-advised with Stéphane Girard (Inria). Limits of extrapolation associated with Bayesian extreme value models.
Minh Tri Lê (2020-), Cifre Ph.D. thesis at TDK InvenSense, co-advised with Etienne De Foras. Constrained signal processing using deep neural networks for MEMs sensors-based applications.
Giovanni Poggiato (2019-), LECA, Inria, co-advised with Wilfried Thuiller (LECA). Scalable Approaches for Joint Species Distribution Modeling.
Daria Bystrova (2019-), LECA, Inria, co-advised with Wilfried Thuiller (LECA). Joint Species Distribution Modeling: Dimension reduction using Bayesian nonparametric priors.
Alumni
Postdoc
Hongliang Lü (2017-2019) co-advised with Florence Forbes
Marta Crispino (2018-2019) co-advised with Stéphane Girard
PhD
Mariia Vladimirova (2018-), Université Grenoble-Alpes, co-advised with Jakob Verbeek (Inria). Prior specification for Bayesian deep learning models and regularization implications.
Fabien Boux (2017-2020) co-advised with Florence Forbes (Inria) and Emmanuel Barbier (GIN).
Master
Louise Alamichel (2021) Université Paris-Saclay, Orsay, co-advised with Daria Bystrova and Guillaume Kon Kam King. Asymptotic properties of Bayesian nonparametric mixture models.
Tony Zhang (2020) Trinity College Dublin, co-advised with Stéphane Girard. Bayesian extreme value models.
Sharan Yalburgi (2019) Birla Institute of Technology and Science, India (BITS). Bayesian deep learning for model selection and approximate inference.
Fatoumata Dama (2019) Université Grenoble-Alpes, co-advised with Jean-Baptiste Durand and Florence Forbes, Bayesian nonparametric models for hidden Markov random fields on count variables and application to disease mapping.
Caroline Lawless (2018) Trinity College Dublin. An elementary derivation of the Chinese restaurant process from the stick-breaking representation for the Pitman--Yor process.
Mariia Vladimirova (2018) Université Grenoble-Alpes, co-advised with Pablo Mesejo (Inria). Wide limit of deep Bayesian neural networks.
Aleksandra Malkova (2018) Université Grenoble-Alpes, co-advised with Maria Laura Delle Monache. DATASAFE: understanding Data Accidents for TrAffic SAFEty.
Michal Lewandowski (2018) Université Grenoble-Alpes. Theoretical properties of Bayesian nonparametric clustering.
Cecilia Ferrando (2016) Collegio Carlo Alberto, Moncalieri, Italy. Bayesian stochastic blockmodels.