|Research Director, Salesforce Research
Co-Director, Stanford Vision and Learning Lab
Adjunct Professor, CS Dept., Stanford University
I’m hiring research interns at Salesforce Research! PhD candidates (or exceptional MS students with first author papers) are welcome to apply to join my team in the areas of video and event understanding, multimodal video+language learning, instructional video parsing, composable models, and/or related topics.
Juan Carlos Niebles received an Engineering degree in Electronics from Universidad del Norte (Colombia) in 2002, an M.Sc. degree in Electrical and Computer Engineering from University of Illinois at Urbana-Champaign in 2007, and a Ph.D. degree in Electrical Engineering from Princeton University in 2011. He is Research Director at Salesforce and Adjunct Professor of Computer Science at Stanford since 2021. He is co-Director of the Stanford Vision and Learning Lab. Before that, he was Associate Director of Research at the Stanford-Toyota Center for AI Research and a Senior Research Scientist at the Stanford AI Lab between 2015 and 2021. He was also an Associate Professor of Electrical and Electronic Engineering in Universidad del Norte (Colombia) between 2011 and 2019. His research interests are in computer vision and machine learning, with a focus on visual recognition and understanding of human actions and activities, objects, scenes, and events. He serves as Area Chair for the top computer vision conferences CVPR and ICCV, as well as Associate Editor for IEEE TPAMI. He is also a member of the AI Index Steering Committee and is the Curriculum Director for Stanford-AI4ALL. He is a recipient of a Google Faculty Research award (2015), the Microsoft Research Faculty Fellowship (2012), a Google Research award (2011) and a Fulbright Fellowship (2005).
My research work is in computer vision. The goal of my research is to enable computers and robots to perceive the visual world by developing novel computer vision algorithms for automatic analysis of images and videos. From the scientific point of view, we tackle fundamental open problems in computer vision research related to the visual recognition and understanding of human actions and activities, objects, scenes, and events. From the application perspective, we develop systems that solve practical world problems by introducing cutting-edge computer vision technologies into new application domains.