Team

For further info about Statify team members, visit the members' page.

Alexandre Wendling (2023-2026) Inria-UGA, co-advised with Clovis Galiez.
Mohamed Bahi Yahiaoui (2022-2025) Inria-CEA, co-advised with Loïc Giraldi and Geoffrey Daniel. Computation time reduction and efficient uncertainty propagation for fission gas simulation.
Julien Zhou (2022-2025) Inria-Criteo, co-advised with Pierre Gaillard and Thibaud Rahier. Learning in Bayesian bandit models.
Louise Alamichel (2021-2024) Inria, co-advised with Guillaume Kon Kam King. Bayesian Nonparametric methods for complex genomic data.

Alumni

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.
Hongliang Lü (2017-2019) co-advised with Florence Forbes
Marta Crispino (2018-2019) co-advised with Stéphane Girard

Théo Moins (2020-2023), Inria, co-advised with Stéphane Girard (Inria). Bayesian computational methods for estimating extreme quantiles from environmental data. [tweet] [manuscript]
Minh Tri Lê (2020-2023), Cifre Ph.D. thesis at TDK InvenSense, co-advised with Etienne De Foras. Constrained deep neural networks for MEMS sensor-based applications. [tweet] [manuscript]
Daria Bystrova (2019-2023), LECA, Inria, co-advised with Wilfried Thuiller (LECA). Bayesian learning of species associations. [tweet]
Giovanni Poggiato (2019-2023), LECA, Inria, co-advised with Wilfried Thuiller (LECA). Integrating ecological dependence into biodiversity modelling.
Mariia Vladimirova (2018-2022), 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).

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.