Qibin Zhao (Ph.D.)
Unit Leader


  • Unit Leader
    Qibin Zhao
  • Postdoctoral Researcher
    Chao Li
  • Postdoctoral Researcher
    Ming Hou
  • Postdoctoral Researcher
    Ning Zheng
  • Senior Visiting Scientist
    Andrzej Cichocki
  • Visiting Scientist
    Cesar Caiafa
  • Visiting Scientist
    Jianting Cao
  • Visiting Scientist
    Toshihisa Tanaka
  • Visiting Scientist
    Tatsuya Yokota


Laboratory's photo

We study various tensor-based machine learning technologies, e.g., tensor decomposition, multilinear latent variable model, tensor regression and classification, tensor networks, deep tensor learning, and Bayesian tensor learning, with aim to facilitate the learning from high-order structured data or large-scale latent space. Our goal is to develop innovative, scalable and efficient tensor learning algorithms supported by theoretical principles. The novel applications in computer vision and brain data analysis will also be explored to provide new insights into tensor learning methods.

Main Research Field
Computer Science
Research Field
Engineering / Neuroscience & Behavior / Mathematics
Research Subjects
Tensor Decomposition and Tensor Networks
Bayesian Tensor Learning
Deep Tensor Learning
Laboratory Website URL

[Poster] FY2018 Research Results

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posted on September 11, 2019 12:00Information