Quentin Garrido is a Member of Technical Staff at AMI Labs, where his research focuses on advanced machine intelligence. His work spans self-supervised learning, computer vision and world models, including the challenge of learning representations and intuitive physical structure directly from raw images and video.

Before joining AMI Labs, Garrido was a research scientist at Meta FAIR, where he also completed a PhD in joint supervision with Université Gustave Eiffel under the supervision of Yann LeCun and Laurent Najman. His research has examined the relationship between contrastive and non-contrastive learning, methods for evaluating self-supervised representations without labels, equivariant representation learning, and the use of world models in visual learning. His publications include RankMe, presented orally at ICML 2023, and a study of contrastive and non-contrastive self-supervised learning that received an Outstanding Paper Honorable Mention at ICLR 2023.

Garrido holds a PhD in computer science from Université Gustave Eiffel, a master’s degree from the Mathematics, Vision and Learning program at ENS Paris-Saclay, and an engineering degree in computer science from ESIEE Paris, where he graduated first in his class. His earlier work applied deep learning and graph-based methods to biological data, earning him the 2022 Ian Lawson Van Toch Memorial Award for an outstanding student paper on visualizing cellular development hierarchies in single-cell RNA sequencing data.