Zhen Fang (Lecturer at UTS-AAII)

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Zhen Fang

Zhen Fang

Lecturer (Assistant Professor) @ DeSI Lab,
 ARC Discovery Early Career Researcher Awardee,
 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 RA/Research Master/PhD. Please use the subject format [Name_Position_AppliedUniversity]. For example, [Jack_PhD_UoM], [Mike_PhD_HKU].

If we match, I will provide a full scholarship（AU$152,000 (after tax)）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