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Feng Liu, Ph.D.

Assistant Professor in Statistics (Data Science),
School of Mathematics and Statistics, 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 108, Old Geology Building (South Wing),
Building #156, Monash Road, Parkville VIC 3052, 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]

Opportunities

Long-time Recruitment: I am always looking for self-motivated PhD (two per semester), MPhil, Research Assistants, and Visiting Researchers. Please see this page for recruiting information, and check this page for the school information. Meanwhile, I am happy to host remote research trainees. You can collaborate with many excellent researchers in the frontier machine learning research areas in our group. Check this page for more information.
Project-based Recruitment:

[Posdoc] We are looking for one Posdoc Research Fellow for our ARC DP project: Advanced Machine Learning with Bilevel Optimization. You will work at UTS with A\Prof Guangquan Zhang and work at UoM with me (as a visiting scholar).
[Posdoc] We are looking for one Posdoc Research Fellow for our NSF-CSIRO project: Graph Representation Learning for Fair Teaming in Crisis Response.
[PhD] We are looking for one PhD student for our ARC DP project: Advanced Machine Learning with Bilevel Optimization. You will work with me as a UoM PhD student. Please see this page for recruiting information.
[3 x RAs] We are looking for three RAs for our NSF-CSIRO project: Graph Representation Learning for Fair Teaming in Crisis Response.

Biography

the School of Mathematics and Statistics,

The University of Melbourne

page

RIKEN-AIP

Australian Artificial Intelligence Institute,

University of Technology Sydney

Dist. Prof. Jie Lu

Prof. Guangquan Zhang

RIKEN-AIP

Prof. Masashi Sugiyama

Dr. Gang Niu

Dr. Bo Han

Gatsby Computational Neuroscience Unit

UCL

Prof. Arthur Gretton

Dr. Danica J. Sutherland

Dr. Wenkai Xu

Research Interests

statistical hypothesis testing

trustworthy machine learning

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).