Zhen Fang (UTS-AAII)

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Zhen Fang

Zhen Fang

Lecturer/Assistant Professor @ DeSI Lab,
 ARC Discovery Early Career Researcher Awardee,
 ACS Australasian AI Emerging Research Awardee,
 NeurIPS 2022 Outstanding Paper Awardee (First Author),
 Australian Artificial Intelligence Institute,
 University of Technology Sydney, Australia

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

I am always looking for highly self-motivated RA/Research Master/PhD students. Thanks!

Drop me an email if you are interested in my Research Master/PhD. Please use the subject format [Name_Position_AppliedUniversity].

If we match, I will provide a full scholarship to support your PhD study.

Biography

    I earned my PhD from the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS), Australia. I am a member of the Decision Systems and e-Service Intelligence (DeSI) Research Laboratory at the Australian Artificial Intelligence Institute, UTS. Currently, I serve as a lecturer at UTS. My research interests include transfer learning and out-of-distribution learning. I have published numerous papers in top-tier conferences and journals, including NeurIPS, ICML, ICLR, IEEE TPAMI, and JMLR, focusing on out-of-distribution learning and transfer learning. My first-author paper received the NeurIPS 2022 Outstanding Paper Award, and I have also been honored with the ARC Discovery Early Career Researcher Award.

Research Interests

    My research interests lie in Machine Learning, Transfer Learning, Statistical Learning Theory, Generalized Out-of-Distribution Learning, Foundation Models. Specifically, my current research work center around four 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.

  • Generalized Out-of-Distribution Learning: Learning a generalized well model or learning a model with OOD detection abaility.

  • Foundation Models: Exploring the basic theory and algorithms for foundation models.

Research Experience

  • Lecturer (Research Intensive) (02.2023--11.2023)

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

  • Postdoc (07.2021--02.2023)

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

  • Research Assistant (06.2021--07.2021)

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

Education

  • Ph.D. in Computer Science (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 Science (2017)

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

  • Bachelor of Science (2014)

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

Sponsors

Australian Research Council