[visionlist] SAVE THE DATE: JIVP Webinar by Enzo Tartaglione, "A journey through Deep Neural Network Debiasing" (February 3, 2026 at 2PM CET)

[Giuseppe Valenzise]

(Apologies if you receive multiple copies of this message)

The next JIVP webinar will take place on Tuesday, February 3, 2026 at 2:00 PM, with Prof. Enzo Tartaglione (Telecom Paris, France).

RSVP to join here: https://cassyni.com/events/ASuSPK8i2nMToFKbmm1FLy?cb=0.az89

Title: A journey through Deep Neural Network Debiasing

Abstract: 
Deep neural networks frequently encode and propagate biases arising from data, model design, and deployment contexts, posing significant challenges for reliable and lawful AI systems. This presentation surveys debiasing techniques for deep neural networks, situating them within the regulatory constraints introduced by the AI Act. We compare supervised and unsupervised debiasing methods, emphasizing their underlying assumptions, optimization objectives, and practical trade-offs. The talk then addresses bias discovery and bias naming as foundational problems for systematic bias mitigation. Finally, we discuss emerging research directions, including privacy-aware debiasing, fairness certification, and machine unlearning, highlighting open technical challenges and unresolved tensions between fairness, robustness, and compliance.

Bio:
Enzo Tartaglione is a Full Professor at Télécom Paris, where he is responsible for the equipe Multimedia and he is a Hi!Paris associate member. He is also a Member of the ELLIS Society, Senior IEEE Member, Associate Editor of IEEE Transactions on Neural Networks and Learning Systems, Action Editor for Transactions of Machine Learning Research and of the EURASIP Journal on Image and Video Processing. He received the MS degree in Electronic Engineering at Politecnico di Torino in 2015, cum laude. The same year, he also received a magna cum laude MS in electrical and computer engineering at University of Illinois at Chicago. In 2016 he was also awarded the MS in Electronics by Politecnico di Milano, cum laude. In 2019 he obtained a PhD in Physics at Politecnico di Torino, cum laude, with the thesis "From Statistical Physics to Algorithms in Deep Neural Systems". His principal interests include compression and responsible (frugal) AI, privacy-aware learning, data debiasing, and regularization for deep learning.


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Dr. Giuseppe Valenzise
CNRS Senior Researcher (Directeur de recherche)
Université Paris-Saclay — CentraleSupelec — CNRS
Laboratoire des Signaux et Systèmes (L2S) — UMR 8506
3, rue Joliot Curie
91192 Gif-sur-Yvette Cedex, France
https://l2s.centralesupelec.fr/u/valenzise-giuseppe/

Editor in Chief Journal on Image and Video Processing (Springer)


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“Immersive Video Technologies”
https://www.elsevier.com/books/immersive-video-technologies/valenzise/978-0-323-91755-1


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