Dr Yadan Luo
Dr

Yadan Luo

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Background

Yadan Luo is currently a Senior Lecturer with Data Science Discipline, School of EECS, The University of Queensland. She received her BSc degree from University of Electronic Science and Technology of China, and her PhD in Computer Science from School of ITEE, The University of Queensland in 2017 and 2021 respectively. Her research interests mainly include machine learning from imperfect data, by leveraging domain adaptation, domain generalization, few-/zero-shot learning and active learning to empower the applications in computer vision and multimedia data analysis areas. Her work of image analysis published at Pattern Recognition Journal in 2018 is placed in the top 1% of the academic field of Engineering and is recognised as a Highly Cited Paper by Web of Science. Yadan was awarded the Google PhD Fellowship 2020 as a recognition of her research in the machine learning area and her strong potential of influencing the future of technology. She was also a recipient of ICT Young Achiever Award, Women in Technology (WiT.org) 2018 and a few other research awards.

[For Prospective Students] I am continuously looking for highly-motivated Ph.D. students to work on machine learning & multimedia data analysis, specifically for addressing domain shifts and generalisation issues. Please send me your CV if interested.

Availability

Dr Yadan Luo is:
Available for supervision
Media expert

Qualifications

  • Bachelor of Computer Science, University of Electronic Science and Technology of China
  • Doctor of Philosophy, The University of Queensland

Research interests

  • Multimedia Data Analysis

  • Machine Learning

    Domain adaptation, domain generalization

  • 3D Lidar-based Object Detection

Search Professor Yadan Luo’s works on UQ eSpace

61 works between 2016 and 2025
All (61) Journal Article (16) Conference Publication (44) Other Outputs (1)

61 - 61 of 61 works

2016

Conference Publication

Zero-shot hashing via transferring supervised knowledge

Yang, Yang, Luo, Yadan, Chen, Weilun, Shen, Fumin, Shao, Jie and Shen, Heng Tao (2016). Zero-shot hashing via transferring supervised knowledge. 24th ACM Multimedia Conference, MM 2016, Amsterdam, The Netherlands, 15 - 19 October 2016. New York, NY, United States: Association for Computing Machinery. doi: 10.1145/2964284.2964319

Zero-shot hashing via transferring supervised knowledge

Current funding

  • 2025
    Beating the Neural Scaling Law through Affordable Machine Learning
    UQ Foundation Research Excellence Awards
    Open grant
  • 2024 - 2027
    Towards Evolvable and Sustainable Multimodal Machine Learning
    ARC Discovery Early Career Researcher Award
    Open grant
  • 2024 - 2027
    Embracing Changes for Responsive Video-sharing Services
    ARC Discovery Projects
    Open grant
  • 2023 - 2026
    Road Atlas: AI-power platform for automated road distress detection and asset management
    Logan City Council
    Open grant
  • 2023 - 2027
    Analytics for the Australian Grains Industry (AAGI)
    Grains Research & Development Corporation
    Open grant

Past funding

  • 2022 - 2024
    Developing a proof-of-concept self-contact tracing app to support epidemiological investigations and outbreak response (Australia-Korea Joint Call for Joint Research Projects - ATSE Tech Bridge Grant)
    Australian Academy of Technological Sciences and Engineering
    Open grant

Availability

Dr Yadan Luo is:
Available for supervision

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Supervision history

Current supervision

Completed supervision

Enquiries

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