Team

If you would like more info about Statify team members, you can visit the members' page.

Loïc Chalmandrier (2024-2026) Inria. Trait-based neural networks for biodiversity models.
Rafael Mouallem Rosa (2024-2026) Inria. Knightian uncertainty.
Tâm Le Minh (2024-2025) Inria. Variational approach to multimodal optimization.

Alice Chevaux (2024-2027) Inria-UGA, with Sophie Achard and Guillaume Kon Kam King. Density-based graphs modelling: inference, comparison, classification.
Camille Touron (2024-2027) Inria-UGA, with Pedro L. C. Rodrigues. Hierarchical posterior estimation with deep generative models: theory and methods.
Eloïse Touron (2024-2027) Inria-UGA, with Michael Arbel, Pedro L. C. Rodrigues and Nelle Varoquaux. Surrogate modeling for simulation-based inference.
Alexandre Wendling (2023-2026) Inria-UGA, co-advised with Clovis Galiez. Machine learning of embeddings and generative models. Applications in ecology.
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 combinatorial bandit models under privacy constraints.

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

Louise Alamichel (2021-2024) Inria, co-advised with Guillaume Kon Kam King. Bayesian nonparametric mixture models and clustering.
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). Bayesian Neural Networks' Distributional Properties.
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.