Hello!

I am a PostDoc researcher at Inria Grenoble Rhone-Alpes in the Statify team.

My research mostly focuses on exploring distributional properties of Bayesian neural networks. More specifically, I am interested in explaining the difference between deep learning models of wide and shallow regimes in order to improve the interpretability and efficiency of the models.

I did my graduate studies in Statify and Thoth teams under supervision of Julyan Arbel and Jakob Verbeek. During November 2019-January 2020, I was visiting Duke University and working on prior predictive distributions in BNNs under supervision of David Dunson. Prior to that, I obtained my Bachelor degree at Moscow Institute of Physics and Technology (MIPT) and did the second year of Master program at Grenoble Institute of Technology (Grenoble - INP, Ensimag).

My CV can be found here.

Hobbies: travelling, hiking and playing the ukulele.



E-mail: mariia.vladimirova@inria.fr.
Address: 655 Avenue de l'Europe, 38330 Montbonnot-Saint-Martin, France

Latest news

  • 25-29 April 2022 I am invited to give a talk on Bayesian deep learning at BNP Networking Workshop (Nicosia, Cyprus):

  • 22 June-1 July 2022 I am organizing a session on Bayesian deep learning at ISBA'22 World Meeting (Montreal, Canada).

  • 2 December 2021: I will give a talk on Bayesian deep learning at AI seminar series in ImVia (Dijon, France).

  • 2 November 2021: our paper "Dependence between Bayesian neural network units" is accepted to NeurIPS workshop on Bayesian deep learning (BDL)!

  • 11 Septemter 2021: our paper "Bayesian neural network unit priors and generalized Weibull-tail property" is accepted to ACML 2021! [arXiv]