Trevor Darrell is a Computer Scientist and Professor at the University of California, Berkeley, where he founded and co-leads UC Berkeley’s Berkeley Artificial Intelligence Research (BAIR) Lab, the Berkeley DeepDrive (BDD) Industrial Consortia and the BAIR Commons program.
Darrell is recognized for his significant contributions across several key areas in artificial intelligence, particularly in computer vision and machine learning. In computer vision, Darrell’s work has focused on advancements in object detection, semantic segmentation and feature extraction techniques. His research has also advanced unsupervised learning techniques and adaptive models that improve generalization from limited examples, as well as cross-modal methods that integrate various data types.
Darrell and colleagues created the Caffe deep learning framework, which was released in 2014. Caffe quickly became one of the most widely used platforms for deep learning, particularly in computer vision applications.
Darrell is the author or co-author of papers that have been cited nearly 300,000 times. In 2024, Darrell and his co-authors received three Test of Time awards for exceptionally impactful papers: from the International Conference on Machine Learning for “DeCAF: A Deep Convolutional Activation Feature for Generic Visual Recognition”; the CVPR Longuet-Higgins Prize for “Rich Feature Hierarchies for Accurate Object Detection and Semantic Segmentation”; and from the ACM Special Interest Group on Multimedia for “Caffe: Convolutional Architecture for Fast Feature Embedding.”
Beyond academia, Darrell is an advisor to several ventures, including SafelyYou, Nexar and SuperAnnotate. Previously, Darrell advised Pinterest, Tyzx (acquired by Intel), IQ Engines (acquired by Yahoo!), Koozoo, BotSquare/Flutter (acquired by Google), MetaMind (acquired by Salesforce), Trendage, Center Stage, KiwiBot, WaveOne, DeepScale and Grabango. He also co-founded and serves as President of Prompt AI.
