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.

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.