Professor David Fleet is a global research leader in computer vision, computational neuroscience, and machine learning. Much of his work has revolved around understanding visual motion, and he’s examined this issue at many levels. At the lowest level, David and Allan Jepson first addressed the problem of computing optical flow – how objects seem to move in a sequence of images as a result of object or camera motion. His phase-based optical flow framework broke new ground in the computer vision community, and established David as one of the world’s most elite researchers studying image motion. He and his colleagues also introduced the computer vision community’s first benchmark, long before benchmarks became essential for evaluation. At the intermediate level, David and his colleagues have made fundamental contributions to motion segmentation and tracking – grouping together moving pixels that belong to the same object. And at the highest level, David and his colleagues were the first to combine both human geometry and scene physics to recover complex articulated human motion from a video sequence. David’s interdisciplinary research program has had a profound impact on a range of fields. His work on image motion has taken him to the field of computational neuroscience, where he and his colleagues broke new ground in understanding how motion and depth is estimated in biological vision. His interest in structure and motion led him to the domain of biomedical imaging and electron cryo-microscopy. With his colleagues, David published a number of highly influential methods for determining the structure and motion of macromolecules at atomic-resolution, given noisy 2D images of a 3D biomolecule from a transmission electron microscopy. Their resulting CryoSPARC framework has become the predominant software used for cryo-EM structure determination in structural biology. Moreover, their extensions to the framework were shown to be remarkably effective for membrane proteins, and were key to determining the structure of the SARS-CoV2 spike protein in 2020. David’s research excellence is reflected in over 170 papers published in the most influential venues in computer vision, machine learning, computational neuroscience, and biomedical imaging; his work has been cited over 49,000 times, with an impressive h-index of 84. His record of research excellence is matched by his distinguished record of leadership, both domestic and international. He has served in prominent leadership roles in our most highly respected conferences and journals, he was Chair of his department at the University of Toronto, and he served as Associate Research Director of the Vector Institute for Artificial Intelligence. More recently, he has been leading the Google DeepMind team in Toronto, one of world’s elite industrial AI research labs, where he is developing some of the machine learning and vision community’s leading diffusion models for image generation. Among his many prestigious awards, David received an Alfred P Sloan Research Fellowship in Computational Neuroscience, the Koenderink Prize for fundamental contributions to computer vision that withstood the test of time, the Lifetime Achievement Award from the Canadian Image Processing and Pattern Recognition Society, a Canadian CIFAR Artificial Intelligence Chair, and numerous best paper awards.