Hello, I'm Eva Feillet.

I am currently Associate Professor (Maîtresse de Conférences) at Paris-Saclay University. I am a member of the STL department at LISN laboratory (Laboratoire Interdisciplinaire des Sciences du Numérique > Sciences et Technologies des Langues > Sémantique et Extraction d'Information), and I teach at (UFR Sciences d'Orsay).

Previously, I was a postdoctoral researcher in frugal deep learning at Université Paris-Dauphine, where I worked with Pr. Alexandre Allauzen in the MILES team of LAMSADE laboratory.
During this postdoc, I was part of the SHARP research project, funded by the France 2030 program and managed by the ANR in the context of PEPR IA.

I did my PhD in continual learning at CEA-list / CentraleSupélec MICS in Paris area, France. During my PhD, I worked under the direction of Pr. Céline Hudelot (CentraleSupélec MICS). I was also supervised by Dr. Adrian Popescu (CEA-list) and Dr. Marina Reyboz (CEA-list).
I hold an engineering degree from CentraleSupélec Engineering school (equivalent to M.Sc. in AI).

My current research interests include :
- Resource-efficient training of deep neural networks, including continual learning
- Visual representation learning and semantic aspects in computer vision
- Speech processing

Current affiliation : LISN - Laboratoire Interdisciplinaire des Sciences du Numérique, Université Paris-Saclay, France

Send me an email at name [dot] surname [at] universite-paris-saclay.fr.
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Visit my profile on GitHub Github and Google Scholar Scholar

Publications

ICCVw 2025

Colagrande, A., Caillon, P., Feillet, E., Allauzen, A.
Linear Attention with Global Context: A Multipole Attention Mechanism for Vision and Physics

In: ECLR Workshop @ ICCV 2025 (2nd Workshop on Efficient Computing under Limited RResources).

[paper] [code]

Thesis manuscript

Feillet, E.
Analysis and Recommendation Methods for Class-Incremental Learning

Université Paris-Saclay, 2024

[access on HAL]

WACV 2025

Feillet, E., Popescu, A., & Hudelot, C.
A Reality Check on Pre-training for Exemplar-free Class-Incremental Learning

In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2025. p 7614-7625.

[paper] [supp]

WACV 2025

Pegeot, T., Feillet, E., Popescu A., Kucher I., & Delezoïde,s B.
Temporal Dynamics in Visual Data : Analyzing the Impact of Time on Classification Accuracy

In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2025. p 6932-6943.

[paper] [supp] [data]

ICPR 2024

Feillet, E., Popescu, A., & Hudelot, C.
Recommendation of data-free class-incremental learning algorithms by simulating future data

[paper][supp]

In: Proceedings of the International Conference on Pattern Recognition. 2024.

WACV 2024

Feillet, E., Petit, G., Soumm M., Popescu, Delezoïde, B., Picard, D., & Hudelot, C.
An Analysis of Initial Traning Strategies for Exemplar-free Class-Incremental Learning

In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2024. p. 1837-1847.

[paper] [supp]

WACV 2023

Feillet, E., Petit, G., Popescu, A., Reyboz, M., & Hudelot, C.
AdvisIL - A Class-Incremental Learning Advisor

In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. 2023. p. 2400-2409.

[paper] [supp] [code]

RFIAP 2022

Feillet, E., Petit, G., Popescu, A., Reyboz, M., & Hudelot, C.
Incremental Learning under Memory Constraints: a scaling heuristic for deep convolutional models

In : Reconnaissance des Formes, Image, Apprentissage et Perception. 2022.

[paper] [code]

Teaching at Université Paris-Saclay

Currently, I teach the following courses at Université Paris-Saclay :
- Math for Data Science (M1, lectures and tutorials)
- Computer vision (M2, lectures, tutorials and projects), based on the course by Céline Hudelot and Maria Vakalopoulou (MICS)
- Frugal learning (M2, projects), with Adrian Popescu (CEA list)
- Introduction to machine learning (L3, tutorials), taught by François Landes (LISN)
- Introduction to data science (L1, tutorials), taught by Fanny Pouyet (LISN)
- AI research project supervision (TER, M1)

Previsously, I have been a teaching assistant at CentraleSupelec for the following courses:
- Deep learning course (Master 2), taught by Hervé Le Borgne (CEA list)
- Information systems and Programming (L3/Bachelor), taught by Gianluca Quercini (LISN)
- Coding weeks challenge (L1/Bachelor), taught by Paul Tourniaire (joint Bachelor CentraleSupélec + McGill University)

Participation to events

Thesis committee

Member of Antoine Montmaur's thesis jury , PhD directed by Pr. Ngoc Son Vu at ETIS Lab
Cergy (France), December 2025

JDSE 2021

Junior Conference on Data Science and Engineering.
Gif-sur-Yvette (France), September 2021.
Best student paper.

Job offers

Research internship 2026 at LISN lab.
Topic: Multimodal continual learning.
Duration: 5 to 6 months (starting February/March 2026).
Location: LISN lab, Université Paris-Saclay, Orsay, France.
Supervisors: Eva Feillet and Sahar Ghanay.
Description: see offer details here
Requirements: Strong background in machine learning and deep learning, experience with deep learning models, proficiency in Python and deep learning frameworks (e.g., TensorFlow, PyTorch), and good communication skills.
Application: Please send your CV, cover letter, transcripts, and any relevant publications or project links.

Get in touch

Email: name [dot] surname [at] universite-paris-saclay.fr