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



  • 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.





  • 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.