Aaron Hertzmann received in Ph.D. in computer science at New York University in 2001, and his B.A. in Computer SCience and Art & Art History in 1996 at Rice University. He joined the Department of Computer Science, University of Toronto in 2003 after spending a year at the University of Washington.
Aaron made an audacious start to his career with a pair of startling papers at SIGGRAPH in 2000 and 2001. One, entitled "Style Machines", showed how idiomatic motion patterns from motion capture sequences could be extracted and applied to animated characters. The other, called "Image Analogies," demonstrated that various aspects of artistic, painterly style could be transferred from one image to another. Taken together, these two papers gave clear evidence that machine learning techniques could be effectively applied to difficult problems in computer graphics by extracting important features from data and retargetting it to novel graphical situations.
Both of these papers have gone on to be among the most highly cited papers in computer graphics. For many researchers, these would be career results, but in fact Aaron has repeated this feat several times in several different fields! This includes technical areas such as computational aesthetics, physically based character animation, automatic tracking, texture synthesis, image processing, and appearance model extraction. While diverse in scope, his approach is consistent: Aaron develops generalized computational models of phenomena that typically are extracted from a corpus of empirical evidence. As such, he commonly takes advantage of, and if necessary develops, statistical analytic techniques that find relevant signals within large amounts of high-dimensional data. A shorter way to say this is that Aaron builds simple, usable visual models extracted from extremely complex systems. His results have always been provocative and often astonishing. They have garnered him numerous research awards and citations, including both Sloan and Microsoft fellowships, an MIT Technology Review top 100 innovator citation, and an Ontario Early Researcher Award.
He sits on various editorial boards and has participated in many conference programme committees, including the SIGGRAPH papers committee several times. He has given many invited talks worldwide and has just returned from a research leave at Pixar.
Aaron's innovations have been strongly influential in computer graphics and computer vision, and his research progress continues to accelerate and be more influential as he draws more top students to his research. The Department of Computer Science at the University of Toronto is proud of his accomplishments, are overjoyed with his being presented a CACS/AIC Outstanding Young Computer Science Researcher award, and look forward to more great things to come from Aaron.