Hello, I'm Eva Feillet.

I am currently Assistant Professor (Maîtresse de Conférence en Informatique) at Paris-Saclay University. I am a member of the 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.

My current research interests include :
- Resource-efficient training of deep neural networks, including continual learning
- Visual representation learning and semantic aspects in computer vision
- Multimodal learning, with a focus on vision and language


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).

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

Publications

ICASSP 2026

Feillet, E., Whetten, R., Picard, D., Allauzen, A.
Polynomial mixing for efficient self-supervised speech recognition

In: ICASSP 2026

[paper] [code]

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 Resources).

[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

Job offers


[PhD] Contact me if you are interested in pursuing a PhD on
Small language models: efficient adaptation & continual evaluation;
Continual learning in the era of multimodal foundation models


[M2] Research internships 2026 at LISN lab / Positions filled !
Topic 1: Multimodal continual learning.
Topic 2: Benchmarking Parameter-Efficient Fine-tuning methods for Spoken Language Understanding.
Duration: 5 to 6 months (starting March 2026).
Location: LISN lab, Université Paris-Saclay, Orsay, France.
Supervisors: Eva Feillet and Sahar Ghannay.

Get in touch

Email: name [dot] surname [at] universite-p#ris-s#clay.fr