Visual Narrative Tianfu Wu
Associate Professor, Electrical and Computer Engineering
Tianfu Wu joined NC State in August 2016 as a Chancellor’s Faculty Excellence Program cluster hire in Visual Narrative. Wu, an associate professor in the Department of Electrical and Computer Engineering, researches interpretable Visual Modeling, Computing and Learning (iVMCL), often motivated by the tasks of pursuing a unified framework for machines to ALTER (Ask, Learn, Test, Explain and Refine) recursively in a principled way. Recently, his work has focused on the following: (i) Interpretable and Universal Representation Learning via Designing and/or Searching Deep Grammar Networks. This line of research is motivated by “the belief that thinking of all kinds requires grammars” and “Grammar in language is merely a recent extension of much older grammars that are built into the brains of all intelligent animals to analyze sensory input, to structure their actions and even formulate their thoughts.” — Professor David Mumford. (ii) Parsimonious and Emergent Representation Learning via Building a Deep Cooperative and Compositional Learning-to-Learn Framework. On top of the research in (i), this line of research is motivated by exploring and exploiting a rich set of tasks organized under principled grammars (i.e., task hierarchy and calculus) to learn-to-learn small details and rich knowledge from little data. Equipped with the two paradigms of representation learning, a unified ALTER framework is envisioned. His research has been supported by ARO, NSF, AFRL and Salesforce.
Wu received his associate degree in electronic engineering and information science from the University of Science and Technology of China; his Master of Science in signal and information processing from Hefei University of Technology 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 the IEEE Transactions on Pattern Analysis and Machine Intelligence and the International Journal of Computer Vision) and conferences (including IEEE Conference on Computer Vision and Pattern Recognition, International Conference on Computer Vision, European Conference on Computer Vision and International Conference on Machine Learning). He served as Presentation Co-Chair at CVPR 2019. He serving as associate editor for the Journal of Image and Vision Computing. He has supervised and mentored graduate and undergraduate students who were interested in computer vision and machine learning at UCLA and NC State.
