Modern technological advances produce data at breathtaking scales and complexities such as the images and videos on the web. Such big data require highly expressive models for their representation, understanding and prediction. To fit such models to the big data, it is essential to develop practical learning methods and fast inferential algorithms. My research has been focused on learning expressive hierarchical models and fast inference algorithms with homogeneous representation and architecture to tackle the underlying complexities in such big data from statistical perspectives. In this talk, with emphasis on a visual restricted Turing test -- the grand challenge in computer vision, I will introduce my work on (i) Statistical Learning of Large Scale and Highly Expressive Hierarchical Models from Big Data, and (ii) Bottom-up/Top-down Inference with Hierarchical Models by Learning Near-Optimal Cost-Sensitive Decision Policies. Applications in object detection, online object tracking and robot autonomy will be discussed.
Bio: Matt Tianfu Wu is currently a research assistant professor in the center for vision, cognition, learning and autonomy (VCLA) at University of California, Los Angeles (UCLA). He received a Ph.D. in Statistics from UCLA in 2011 under the supervision of Prof. Song-Chun Zhu. His research has been focused on computer vision and robot autonomy from the perspective of statistical modeling, inference and learning: (i) Statistical learning of large scale and highly expressive hierarchical and compositional models from visual big data (images and videos). (ii) Statistical inference by learning near-optimal cost-sensitive decision policies. (iii) Statistical theory of performance guaranteed learning algorithm and optimally scheduled inference procedure. (iv) Statistical framework of a restricted vision Turing test and life-long learning for robot autonomy.