Main topics


Statistical Machine Learning, Deep Learning, Neural Networks, Artificial Intelligence


Current projects


Deep Learning For Vocal Brain Analysis

With Pascal Belin, Bruno Giordano and Sylvain Takerkart at INT

Funding : Charly Lamothe’s Ph.D with Pascal Belin’ERC

The project aims at creating a map of the brain areas according to its processing level of input stimuli by comparing cerebral representations with learned representations in a deep neural net.


Comparing computational and cerebral representations of voice and sounds

With Bruno Giordano

Funding: Projet ANR AAPG 2021 BrainSoundSem


A mathematical framework for morpho-transcriptomics

With Paul Villoutreix (LIS

Funding: Malek Senoussi’s Ph.D. funding from Paul Villoutreix’s Centuri chaire


Explainability in Neural Networks for sequence data

With Stephane Ayache (LIS) and Hamed Benazha (LIS)

Funding : Projet ANR AAPG 2020 TAUDOS


Past projects


Sequence modeling, labeling and classification

Design of statistical models for sequence labeling and classification based on Hidden Markov Models, Conditional Random Fields, and variants like Contextual Hidden Markov Models, Hidden Conditional Random Fields, neural extensions of these models etc.


Extreme Classification

Extreme classification means classification in a very large number of classes (up top millions), either in monolabel or multilabel classification settings. This projet consisted in part in designing new methods for this challenging setting and in organizing international large scale classification challenges (LSHTC series and BioAsQ series).


Budgeted Learning

 Budget learning covers various topics. This project aimed at designing classifiers that are economic in the number of features that are required to produce a  decision on an input data.


Adversarial Learning For Animation

With Qi Wang.

Funding : Chinese doctoral program CSC-ECM

We designed synthesis systems for generating motion capture data that matchs some high level  features ( e.g. the emotion with which an motion is performed) using adversarial learning and recurrent neural nets.


Disentangling factors of variations

With Mickael Chen and Ludovic Denoyer at LIP6, UPMC

Disentangling factors of variations aims at learning factors of variations from data. Once learned it allows changing the stye of an input data (e.g. viewpoint for image data) to the style of another image. We have applied such techniques, relying on adversarial learning on images and on motion capture sequences.


Deep Learning For High Energy Physics

With Ziyu Guo and Y. Coadou at CPPM

Funding : Bourse Inter-ED à AMU


Mixing Deep Learning And Kernels

With Luc Giffon, Stéphane Ayache, Hachem Kadri (LIS)

Funding : ANR AAPG 2017 : Deep In France


Multiview Learning

With Mickael Chen (LIP6) and Ludovic Denoyer (Facebook)


Multisource Learning

With Qi Wang and Sylvain Takekart (INT)