Feng Liu (he/him) -- Assistant Professor at The University of Melbourne

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Feng Liu

Feng Liu, Ph.D.

Assistant Professor in Machine Learning,
ARC Discovery Early Career Researcher Awardee (Machine Learning),
ACS Australasian AI Emerging Research Awardee,
School of Computing and Information Systems, The University of Melbourne

Visiting Scientist @ Imperfect Information Learning Team,
RIKEN Center for Advanced Intelligence Project (RIKEN-AIP)

Visting Fellow @ DeSI Lab,
Australian Artificial Intelligence Institute, UTS

Address: Room 3317, Level 3, Melbourne Connect (Building 290),
700 Swanston Street, University of Melbourne VIC 3010 Australia.
E-mail: fengliu.ml [at] gmail.com or feng.liu1 [at] unimelb.edu.au
Phone: +61 3 9035 3645
[Google Scholar] [Github] [Group Website] [CV]

Opportunities

  • Long-time Recruitment (PhD): I am always looking for self-motivated PhD (two per semester). Please see this page for recruiting information, and check this page for the school information. Update (Aug. 15, 2024): No PhD position is available before July 2025.

  • Long-time Recruitment (RA): Meanwhile, I am happy to host remote research trainees (paied). You can collaborate with many excellent researchers in the frontier machine learning research areas in our group. Check this page for more information. Update (Aug. 15, 2024): We are actively recruiting RAs. Please send your CV to my email (Gmail).

  • Project-based Recruitment:

    • No active recruitment.

Biography

    I am a machine learner with research interests in hypothesis testing and trustworthy machine learning. I am currently an ARC DECRA Fellow and an Assistant Professor in Machine Learning at the School of Computing and Information Systems, The University of Melbourne, Australia. We are also running the Trustworthy Machine Learning and Reasoning (TMLR) Lab where I am one of co-directors (see this page for details). In addition, I am a Visiting Scientist at RIKEN-AIP, Japan, and a Visting Fellow at DeSI Lab, Australian Artificial Intelligence Institute, University of Technology Sydney. I was the recipient of the Australian Laureate postdoctoral fellowship. I received my Ph.D. degree in computer science at the University of Technology Sydney in 2020, advised by Dist. Prof. Jie Lu and Prof. Guangquan Zhang. I was a research intern at the RIKEN-AIP, working on the robust domain adaptation project with Prof. Masashi Sugiyama, Dr. Gang Niu and Dr. Bo Han. I visited Gatsby Computational Neuroscience Unit at UCL and worked on the hypothesis testing project with Prof. Arthur Gretton, Dr. Danica J. Sutherland and Dr. Wenkai Xu.

    I have received the ARC Discovery Early Career Researcher Award, the FEIT Excellence Award in Early Career Research at The University of Melbourne, the Outstanding Paper Award of NeurIPS (2022), the Outstanding Reviewer Award of NeurIPS (2021), the Outstanding Reviewer Award of ICLR (2021), the UTS Best Thesis Award (Dean's list), the UTS-FEIT HDR Research Excellence Award (2019) and the Best Student Paper Award of FUZZ-IEEE (2019). My publications are mainly distributed in high-quality journals or conferences, such as Nature Plants, Nature Communications, JMLR, IEEE-TPAMI, IEEE-TNNLS, IEEE-TFS, NeurIPS, ICML, ICLR, KDD, IJCAI, and AAAI. I have served as area chairs (AC) for ICML, NeurIPS, ICLR, AISTATS, senior program committee (SPC) members for AAAI, IJCAI, ECAI and program committee (PC) members for NeurIPS, ICML, ICLR, AISTATS, ACML, AAAI, IJCAI, KDD, SDM and so on. I also serve as an Editor for ACM TOPML, Associate Editor for IJMLC, Action Editor for Neural Networks, Action Editor for Transactions on Machine Learning Research and reviewers for many academic journals, such as JMLR, IEEE-TPAMI, TMLR, MLJ, and so on.

Research Highlights

Research Interests

    My research interests lie in statistical hypothesis testing and trustworthy machine learning. Specifically, my current research work center around the following topics:
    Statistical Hypothesis Testing:

  • Two-sample Testing: Testing if two datasets are drawn from the same distribution.

  • Goodness-of-fit Testing: Testing if data are drawn from a given distribution.

  • Independence Testing: Testing if two datasets are independent.

    Trustworthy Machine Learning:

  • Defending against Adversarial Attacks: Detecting adversarial attacks (i.e., adversarial attack detection); Training a robust model against future adversarial attacks (i.e., adversarial training).

  • Being Aware of Out-of-distribution Data: Detecting out-of-distribution data; Training a robust model in the open world (e.g., open-set learning, out-of-distribution generalization).

  • Learning/Inference under Distribution Shift (a.k.a., Transfer Learning): Leveraging the knowledge from domains with abundant labels (i.e., source domains)/pre-trained models (i.e., source models) to complete classification/clustering tasks in an unlabeled domain (i.e., target domain), where two domains are different but related.

  • Protecting Data Privacy: Training a model to ensure that the training data will not be obtained by inverting the model (i.e., defending against model-inversion attacks).

Research Experience

Education

  • Ph.D. in Computer Science (November 2020)

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

Sponsors

Australian Research Council CSIRO NSF