I'm interested in algorithms for visual perception
(object recognition, localization, segmentation, pose
estimation, ...) and representation learning
(pre-training networks using strong supervision, weak
supervision, or no supervision at all). My work explores
topics in computer vision and machine/deep/statistical learning.
About me / bio
Ross Girshick is a research scientist in Meta AI's Fundamental AI Research
(FAIR) team, working on computer vision and machine learning. He
received a PhD in computer science in 2012 from the University
of Chicago while working with Pedro Felzenszwalb. Prior to
joining FAIR, Ross was a researcher at Microsoft Research and
a postdoc at the University of California, Berkeley, where he
was advised by Jitendra Malik and Trevor Darrell. His interests
include representation learning and systems for solving computer vision problems that exhibit
broad generalization. He received the 2017 PAMI Young Researcher Award and
the 2017 and 2021 PAMI Mark Everingham Prizes for his open
source software contributions. Ross is well-known for developing
the R-CNN (Region-based Convolutional Neural Network) approach
to object detection, and, in 2017, Ross received the Marr Prize
at ICCV for "Mask R-CNN". Outside of research, Ross is usually
rock climbing and trying to send his latest project.
Journal reviewing note: Please do not invite me to review unless you have asked me via a personal message beforehand (though I will most likely decline). I receive many unsolicited requests per week, which I simply delete without reading due to the volume.
Erdös number = 3 (via two paths)