About me
Hi! I am Eden Belouadah, an ML Research Scientist at Datakalab. I have 5 years of experience working on continual learning for image classification and object detection. At Datakalab, I am part of the context adaptation team. Our goal is to compress large object detection models and deploy them on the edge. I previously worked on fall detection and continuous optimization. Currently, I am also working on Large Language Models (LLMs) and Stable Diffusions. P.S. The website is under construction.
Interests
- Continual Learning
- Object Detection
- Generative AI
- Stable Diffusion
- Large Language Models
- Fall Detection
Education
PhD in Artificial Intelligence, 2021
IMT Atlantique, France
MSc in Machine Learning, 2018
Paris-Saclay University, France
MSc in Artificial Intelligence, 2017
USTHB, Algeria
BSc in Maths and Computer Science, 2015
USTHB, Algeria
Experience
Deep Learning Researcher
- Use compression techniques on object detection models and deploy them on the edge
- Propose continual learning methods for context adaptation without catastrophic forgetting
- Work on large language models and diffusion models
- Write CD/CI pipelines, unit tests, and master VSCode debugging tool
- Deliver on-demand projects to clients
- Write a scientific publication to validate academic results
Researcher Ph.D. Student
- Propose state-of-the-art Continual Learning solutions for image classification
- Write several scientific publications to validate the proposed methods
- Give Deep Learning classes to Master’s students (Centrale Supélec School)
- Give Operating system classes to Bachelor’s students (Paris-Saclay University)
- Supervise Interns and collaborate with other Ph.D. students
Selected Publications
Recent Publications
Quickly discover relevant content by filtering publications.
(2023).
PlaStIL: Plastic and Stable Exemplar-Free Class-Incremental Learning.
Proceedings of The 2nd Conference on Lifelong Learning Agents.
(2023).
MultIOD: Rehearsal-free Multihead Incremental Object Detector.
arXiv preprint arXiv:2309.05334.
(2021).
A comparative study of calibration methods for imbalanced class incremental learning.
Multimedia Tools and Applications.
(2020).
Active Class Incremental Learning for Imbalanced Datasets.
Computer Vision - ECCV 2020 Workshops - Glasgow, UK, August 23-28, 2020, Proceedings, Part VI.
(2018).
DeeSIL: Deep-Shallow Incremental Learning.
TaskCV Workshop @ ECCV 2018..
