Tianfu Wu joined NC State in August 2016 as a Chancellor’s Faculty Excellence Program cluster hire in Visual Narrative. Wu, an assistant professor in the Department of Electrical and Computer Engineering, researches explainable and improvable visual Turing test and robot autonomy through lifelong communicative learning. To accomplish his research goals, he pursues a unified framework for machines to ALTER (Ask, Learn, Test, Explain and Refine) recursively in a principled way. Recently, his work was focused on the following: statistical learning of large scale and highly expressive hierarchical and compositional models from visual big data (images and videos); statistical inference by learning near-optimal cost-sensitive decision policies; statistical theory of performance guaranteed learning algorithm and optimally scheduled inference procedure; and statistical framework of visual Turing test and lifelong communicative learning.
Wu received his associate degree in electronic engineering and information science from the University of Science and Technology of China (USTC); his Master of Science in signal and information processing from Hefei University of Technology (HFUT) in China; and his Ph.D. in statistics from the University of California, Los Angeles (UCLA). He was a postdoctoral researcher in the Department of Statistics at UCLA. Prior to joining the NC State faculty, he was a research assistant professor of statistics in the Department of Statistics at UCLA. His work has been published in top computer vision journals (including Institute of Electrical and Electronics Engineers, Transactions on Pattern Analysis and Machine Intelligence and the International Journal of Computer Vision) and conferences (including Computer Vision and Pattern Recognition, International Conference on Computer Vision and European Conference on Computer Vision). He has supervised and mentored graduate and undergraduate students who were interested in computer vision and machine learning at UCLA and NC State.