PeopleofSuperintelligence

Researcher

Feng Liu

Feng Liu is a machine-learning researcher at the University of Melbourne whose work builds statistical foundations for trustworthy AI, from hypothesis tests to out-of-distribution detection. He earned a PhD at the University of Technology Sydney in 2020 under Jie Lu and Guangquan Zhang, and coauthored Learning Deep Kernels for Non-Parametric Two-Sample Tests with Arthur Gretton and Danica Sutherland at ICML 2020. After joining Melbourne as a lecturer in 2022, he coauthored the NeurIPS 2022 Outstanding Paper "Is Out-of-Distribution Detection Learnable?" with Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong and Bo Han, and an ARC Discovery Early Career Researcher Award followed in 2023. A monograph from his team, Trustworthy Machine Learning: From Data to Models, appeared in 2025, and he secured an ARC Future Fellowship in 2026. He is a Senior Lecturer in Machine Learning at Melbourne and co-directs the Trustworthy Machine Learning and Reasoning Lab.

Timeline

  1. 2019–2020

    Deep kernels for two-sample tests

    In 2019, he is a research intern with Masashi Sugiyama's Imperfect Information Learning Team at RIKEN-AIP in Tokyo, then a visiting researcher with Arthur Gretton at the Gatsby Computational Neuroscience Unit at UCL. In 2020, he publishes Learning Deep Kernels for Non-Parametric Two-Sample Tests at ICML with Wenkai Xu, Jie Lu, Guangquan Zhang, Gretton and Danica Sutherland, proposing tests built on kernels parameterized by deep neural nets and trained to maximize test power.

  2. 2020–2021

    A UTS doctorate and postdoc

    He completes a PhD in computer science in 2020 at the University of Technology Sydney's Faculty of Engineering and Information Technology, supervised by Jie Lu and Guangquan Zhang. From May 2020 to May 2021, he is an Australian Laureate postdoctoral researcher at UTS's Australian Artificial Intelligence Institute, working on Autonomous Transfer Learning with Lu as his advisor.

  3. 2022–2023

    Lecturer at Melbourne

    In 2022, he joins the University of Melbourne's School of Mathematics and Statistics as a Lecturer in Data Science on a fixed-term contract. In 2023, he moves to the School of Computing and Information Systems as a Lecturer in Machine Learning, a continuing position.

  4. 2022

    A NeurIPS Outstanding Paper Award

    His paper "Is Out-of-Distribution Detection Learnable?", written with Zhen Fang, Yixuan Li, Jie Lu, Jiahua Dong and Bo Han, receives a NeurIPS 2022 Outstanding Paper Award. It applies probably approximately correct (PAC) learning theory to out-of-distribution detection, proves impossibility theorems and gives conditions under which detection is learnable.

  5. 2023

    An ARC DECRA fellowship

    In 2023, he receives an Australian Research Council Discovery Early Career Researcher Award (DECRA) in the machine learning category.

  6. April 2025

    A trustworthy machine learning monograph

    In April 2025, he and his team publish the monograph Trustworthy Machine Learning: From Data to Models in Foundations and Trends in Privacy and Security.

  7. 2026

    An ARC Future Fellowship

    In 2026, he secures an ARC Future Fellowship as a sole investigator, the only 2026 Future Fellowship recipient in Victoria in information and computing sciences.

  8. As of October 2026

    Senior Lecturer at Melbourne

    He is a Senior Lecturer in Machine Learning in the University of Melbourne's School of Computing and Information Systems, a tenured post he holds after a 2025 promotion, and co-directs the Trustworthy Machine Learning and Reasoning (TMLR) Lab. His current research centers on data-adaptive hypothesis testing and on certified evaluation of large language models with statistical guarantees. He is Action Editor for Neural Networks (since 2023) and Transactions on Machine Learning Research (since 2024), Program Co-Chair of AJCAI 2026 and Communication Chair of NeurIPS 2026.

Career

  1. Since 2022University of MelbourneLecturer in Data Science, Lecturer in Machine Learning
  2. 2020–2021University of Technology SydneyAustralian Laureate Postdoctoral Researcher
  3. From 2019RIKENResearch Intern
  4. From 2019University College LondonVisiting Researcher

Education

  1. 2020University of Technology SydneyPhD

From the biography.

  1. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other universityIncludes Feng Liu. The Massachusetts Institute of Technology trained or employed 44 of the 187 AI major-prize winners, more than any other university. We compare doctorate and 5+ year employment links, career timing and current workplaces for 187 major-prize and 1,111 best-paper winners.
  2. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionIncludes University of Melbourne. We estimate where the people shaping AI studied and where their careers led. Massachusetts Institute of Technology leads with 467 former students, including 291 recorded doctorates.
  3. The company share of AI researchers’ top papers was 31% in 2020–2024 and 8% in 1970–1974Includes University of Melbourne. We estimate where the people shaping AI worked when their most-cited papers appeared: the company share was 31% in 2020–2024 and 8% in 1970–1974, and Google led all organizations in 2020–2024.
  4. Google has the highest combined h-index among AI organizationsIncludes University of Melbourne. We estimate that Google holds the highest combined h-index at 8,016, while universities account for 73% of the deduplicated total across AI organizations.

Colleagues

People named in this biography, and people with dated positions at the same organization in the same years.

Sources and credits

Photo: We have no published photo of Feng Liu yet. Send us a free-licensed photo.

  1. Feng Liu -- Trustworthy AI | The University of Melbourne fengliu90.github.io. Checked 21 October 2026.
  2. [2002.09116] Learning Deep Kernels for Non-Parametric Two-Sample Tests arxiv.org. Checked 21 October 2026.
  3. Announcing the NeurIPS 2022 Awards – NeurIPS Blog blog.neurips.cc. Checked 21 October 2026.
  4. Is Out-of-Distribution Detection Learnable? proceedings.neurips.cc. Checked 21 October 2026.
  5. Dr Feng Liu : Find an Expert : The University of Melbourne findanexpert.unimelb.edu.au. Checked 21 October 2026.