Latest news

[Mar 26] Article accepted for publicatin at IEEE Transactions on Circuits and Systems for Video Technology: Compression in 3D Gaussian Splatting: A Survey of Methods, Trends, and Future Directions. Muhammad Salman Ali, Chaoning Zhang, Marco Cagnazzo, Giuseppe Valenzise, Enzo Tartaglione, Sung-Ho Bae.

[Feb 26] Paper accepted at CVPR 2026: Bias In, Bias Out? Finding Unbiased Subnetworks in Vanilla Models.

[Dec 25] I have been promoted to the rank of Full Professor

[Nov 25] Article accepted for publication at Neurocomputing: TEP-ones: A Simple yet Effective Approach for Transferability Estimation of Pruned Backbones

[Nov 25] Article accepted for publication at IEEE Transactions on Multimedia: Security and Real-time FPGA integration for Learned Image Compression

Professorships, PhDs and stages applications are now open!

Tenure track position on animation with AI is now open!


Mission

In a world where deep learning is becoming more and more state-of-the-art, where the race to computational capabilities determines new technologies, it is crucial to open the black box that deep learning is. Many researchers are already taking important steps in this direction, despite the wide range of scientific backgrounds. This is good, this is progress!

We target it in the long term, developing techniques that simplify these models. Some are easier to prune than others: why? How is information being processed inside a deep model from a macroscopic perspective? These are a few of the questions to be answered to move in the right direction.

Explore an interactive map of every paper, grouped by research theme.

Open the research graph

Currently working with

PhD student

Dorian Gailhard

Dorian Gailhard

PhD student
Generative models and Graph Neural Networks for SoCs
Co-supervised with Jhony H. Giraldo
Imad Eddine Marouf

Imad Eddine Marouf

PhD student
Efficient transformers for computer vision
Co-supervised with Stéphane Lathuilière
Ivan Luiz De Moura Matos

Ivan Luiz De Moura Matos

PhD student
Debiasing through NAS
Previously: Stage M2 Oct 23-Feb 24
Kian Bakhtari

Kian Bakhtari

PhD student
Efficient foundation models exploitation for autonomous driving
Co-supervised with Stephan Alaniz
Leonardo Magliolo

Leonardo Magliolo

PhD student
Information flow in Deep Neural Networks
Lê Trung Nguyen

Lê Trung Nguyen

PhD student
Methods for on-device training
Co-supervised with Van-Tam Nguyen
Rayyan Ahmed

Rayyan Ahmed

PhD student
Explaining and Removing Social Biases in Text-to-Image Generative AI
Co-supervised with Stephan Alaniz
Salaheldin Mohamed

Salaheldin Mohamed

PhD student
Co-supervised with Kaouther Messaoud
Yinghao Wang

Yinghao Wang

PhD student
Foundation models for EEG treatment
Co-supervised with Van-Tam Nguyen
Zhu Liao

Zhu Liao

PhD student
Deep Neural Network pruning
Co-supervised with Van-Tam Nguyen

Invited PhD student

Andrea Diecidue

Andrea Diecidue

Invited PhD student
Previously: Invited Jun-Sep 2025

Stage M2

Huanshan Huang

Huanshan Huang

Stage M2
Input frame compression for deep video processing
Previously: Stage M1 Apr-Aug 2025
Radjaa Larbi

Radjaa Larbi

Stage M2
3D Gaussian Splatting compression

Stage M1

Daniele Famà

Daniele Famà

Stage M1
Deep Learning energy consumption measure on embedded systems
Yiming Yang

Yiming Yang

Stage M1
3D Gaussian Splatting generation

Formerly advising

PostDoc

Research Engineer

PhD student

Invited PhD student

Research path student

PRIM project

Stage M2

Stage M1

Free stage