Trevor Darrell

Trevor Darrell is a Professor in Residence in the EE department at the University of California, Berkeley. His research focuses on Artificial Intelligence (AI), Control, Intelligent Systems, and Robotics (CIR). His group develops algorithms for visual recognition across various platforms and applications, with interests in computer vision, machine learning, computer graphics, and perception-based human-computer interfaces. Prof. Darrell was previously on the faculty of the MIT EECS department from 1999-2008 and was a member of the research staff at Interval Research Corporation from 1996-1999. He received his S.M. and Ph.D. degrees from MIT in 1992 and 1996, respectively, and his B.S.E. degree from the University of Pennsylvania in 1988.

Trevor Darrell is a Professor of Computer Science at the University of California, Berkeley, specializing in Computer Vision, Artificial Intelligence, AI, Machine Learning, and Deep Learning. His research focuses on developing advanced algorithms for visual recognition, semantic segmentation, and domain adaptation. He has contributed significantly to the field through publications on fully convolutional networks, long-term recurrent convolutional networks, and adversarial feature learning. His work emphasizes the integration of deep learning techniques for real-time applications such as human body tracking and visuomotor policy learning. Darrell's research also explores the use of convolutional architectures for feature extraction and multimodal image-to-image translation.