Zhen Fang (Postdoctoral Research Fellow at UTS-AAII)


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Zhen Fang

Zhen Fang

Postdoctoral Research Fellow @ DeSI Lab,
Australian Artificial Intelligence Institute, UTS

Address: Level 12, UTS Central,
61 Broadway, Ultimo, Sydney, NSW2007, Australia.
E-mail: zhen.fang [at] uts.edu.au [Google Scholar] [Github]


Biography

    I am a machine learning researcher with research interests in transfer learning and machine learning theory. My long-term goals are 1) to develop mathematical theories for understanding why human being can learn knowledge from related but different domains, 2) to develop artificial intelligence algorithms, which can reduce the need of labeled data by learning knowledge from other domains, and 3) to develop intelligent robots that can learn knowledge from different datasets automatically.

    I am currently a Postdoctoral Research Fellow at Decision Systems and e-Service Intelligence (DeSI) Lab, Australian Artificial Intelligence Institute (AAII), University of Technology Sydney (UTS), Australia. I am the recipient of the Australian Laureate postdoctoral fellowship. I have completed my Ph.D. degree in computer science at UTS-AAII in Oct 2021.

    I have served as reviewers for many top-tier conference and journals (CORE, ERA Tier A*/A), such as International Conference on Machine Learning (ICML), Conference on Computer Vision and Pattern Recognition (CVPR), International Joint Conference on Neural Networks (IJCNN), International Joint Conferences on Artificial Intelligence (IJCAI), Association for the Advancement of Artificial Intelligence (AAAI), European Conference on Computer Vision (ECCV), IEEE International Conference on Fuzzy Systems (FUZZ-IEEE) and IEEE Transactions on Neural Networks and Learning Systems (TNNLS).


Research Interests

    My research interests lie in Machine Learning, Transfer Learning, Statistical Learning Theory, Computer Science Theory, Quantum Computation, Partial Differential Equations . Specifically, my current research work center around three major topics:
  • Transfer Learning: Transferring knowledge from a source domain to a target domain.

  • Statistical Learning Theory: Estimating the generalization error of a given problem or algorithm.

  • Weakly Supervised Learning: Learning classification model from an incomplete knowledge.


Research Experience

  • Research Assistant (June 2021--Aug 2021)

  • Advisor: Dist. Prof. Jie Lu
    Project: Transfer Learning


Education

  • Ph.D. in Computer Science (June 2021)

  • Faculty of Engineering and Information Technology,
    University of Technology Sydney, Sydney, Australia.
    Supervised by Dist. Prof. Jie Lu and Prof. Guangquan Zhang

  • Master of Pure Mathematics (July 2017)

  • School of Mathematical Sciences, Xiamen University, Xiamen, China
    Supervised by Prof. Bo Guan

  • Bachelor of Science (June 2013)

  • School of Mathematic and Statistics and Cuiyin Honors College, Lanzhou University, Lanzhou, China


Sponsors

Australian Research Council