Rahaf Aljundi

I am interested in building machines that can keep learning (given any source of supervision), can tell when a novel input is provided and further point at instances that require annotation.
This includes the topics of continual learning ( incremental learning, lifelong learning), novelty detection ( out of distribution, anomaly detection), active learning and also domain adaptation.
I currently work at Toyota Motor Europe as a researcher in the Artificial intelligence team.
I have a PhD degree from KU Leuven university (Belgium) where I worked on the topic of continual learning under the supervision of prof. Tinne Tuytelaars.

• Our paper on Reducing representation drift in online continual learning is accepted at ICLR 2022.
• Our Active Learning paper is accepted at WACV 2021.
• Two papers accepted at ICCV 2021.
• I gave a continual learning tutorial at NeSy 2021, part of IJCLR 2021.
• I gave a talk at the CVPR continual learning workshop 2021.
• Our continual learning survey has been accepted at TPAMI.
• I am serving as an Area chair for CVPR 2021.