I'm interested in algorithms for visual perception (object recognition, localization, segmentation, pose estimation, ...), representation learning (pre-training networks using strong supervision, weak supervision, or no supervision at all), and the interaction of vision and language. My work explores topics in computer vision and machine/deep/statistical learning.

Ross Girshick is a Distinguished Research Scientist at the Allen Institute for Artificial Intelligence (AI2). He received a PhD in computer science in 2012 from the University of Chicago while working with Pedro Felzenszwalb. Before joining AI2, Ross was a Research Scientist in Meta's Fundamental AI Research (FAIR) team working on computer vision and machine learning (2015-2023), a Researcher at Microsoft Research (2014-2015), and a postdoc at the University of California, Berkeley, where he was advised by Jitendra Malik and Trevor Darrell (2012-2014). His current research interests include systems for solving problems involving vision, language, and action that exhibit broad generalization. Ross received the 2017 PAMI Young Researcher Award and is a three-time winner of the PAMI Mark Everingham Prize (2017, 2021, and 2023) for his contributions to open source software and datasets. Ross is well-known for developing the R-CNN (Region-based Convolutional Neural Network) approach to object detection. The R-CNN line of work led to "Mask R-CNN" which received the Marr Prize at ICCV in 2017. In 2024, R-CNN was recognized with a 10-year test of time award (the Longuet-Higgins Prize). Outside of research, Ross is usually rock climbing and trying to send his latest project.