Projects per year
Personal profile
Biography
James Bailey is a Professor and Head of Department of Data Science and Artificial Intelligence at Monash University. He has previously been an Australian Research Council Future Fellow and is a researcher in the field of machine learning and artificial intelligence, including interdisciplinary applications and operational frameworks. His interests particularly relate to the assurance, certification and safety of systems based on machine learning and artificial intelligence. He works on the deployment of AI systems in collaboration with a wide range of industry and government partners
(Google Scholar: https://scholar.google.com/citations?user=ujsYC98AAAAJ&hl=en)
Education/Academic qualification
Computer Science, PhD, University of Melbourne
Award Date: 1 Feb 1998
Research area keywords
- data science
- data mining
- machine learning
- data analytics
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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SDG 3 Good Health and Well-being -
SDG 4 Quality Education -
SDG 7 Affordable and Clean Energy -
SDG 11 Sustainable Cities and Communities -
SDG 13 Climate Action -
SDG 15 Life on Land
Projects
- 3 Active
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TMR: Trustworthy Model Reprogramming: Learning with Imperfect Pre-trained Models
Liu, F. (Primary Chief Investigator (PCI)), Bailey, J. (Chief Investigator (CI)), Cui, T. (Chief Investigator (CI)), Song, Y. (Chief Investigator (CI)), Li, S. (Partner Investigator (PI)) & Chen, K. (Partner Investigator (PI))
ARC - Australian Research Council
8/07/26 → 7/07/29
Project: Research
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Next Generation Spatial Data Management for Virtual Spatial Systems
Kulik, L. (Primary Chief Investigator (PCI)), Bailey, J. (Chief Investigator (CI)), Cao, X. X. (Chief Investigator (CI)), Jensen, C. (Partner Investigator (PI)) & Wang, W. (Partner Investigator (PI))
ARC - Australian Research Council
1/05/23 → 30/04/27
Project: Research
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Predict‐and‐Optimise through Time at Water Recycling Treatment Plants
Stuckey, P. (Primary Chief Investigator (PCI)), Bailey, J. (Chief Investigator (CI)), Leckie, C. A. (Chief Investigator (CI)), Crook, J. (Partner Investigator (PI)) & Bergmann, D. (Partner Investigator (PI))
8/12/21 → 8/12/26
Project: Research
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A Bayesian framework for robust local intrinsic dimensionality estimation
Joukhadar, Z., Huang, H., Erfani, S. M., Campello, R. J. G. B., Houle, M. E. & Bailey, J., Jun 2026, In: Information Systems. 138, 19 p., 102668.Research output: Contribution to journal › Article › Research › peer-review
Open AccessFile1 Link opens in a new tab Citation (Scopus) -
Coarse-to-Fine Open-Set Graph Node Classification with Large Language Models
Ma, X., Ma, X., Erfani, S. M., Mandic, D. & Bailey, J., 2026, Proceedings of the AAAI Conference on Artificial Intelligence. Koenig, S., Jenkins, C. & Taylor, M. E. (eds.). 43 ed. Washington DC USA: Association for the Advancement of Artificial Intelligence (AAAI), p. 36714-36722 9 p. (Proceedings of the AAAI Conference on Artificial Intelligence; vol. 40, no. 43).Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
Open Access -
DynaPURLS: Dynamic Refinement of Part-Aware Representations for Skeleton-Based Zero-Shot Action Recognition
Zhu, J., Zhu, A., Bailey, J., Liu, J., Rahmani, H., Bennamoun, M., Boussaid, F. & Ke, Q., Aug 2026, In: IEEE Transactions on Pattern Analysis and Machine Intelligence. 48, 8, p. 9659-9674 16 p.Research output: Contribution to journal › Article › Research › peer-review
1 Link opens in a new tab Citation (Scopus) -
Intrinsic Dimension, Degrees of Freedom, Odds and Uniformity: A Unified Perspective
Bailey, J., Campello, R. J. G. B. & Houle, M. E., 2026, Similarity Search and Applications - 18th International Conference, SISAP 2025 Reykjavik, Iceland, October 1–3, 2025 Proceedings. Amato, G., Mic, V., Traina, A., Messina, N., Amsaleg, L., Þór Guðmundsson, G., Þór Jónsson, B. & Vadicamo, L. (eds.). Cham Switzerland: Springer, p. 71-84 14 p. (Lecture Notes in Computer Science; vol. 16134).Research output: Chapter in Book/Report/Conference proceeding › Conference Paper › Research › peer-review
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Learnable Coreset Selection for Graph Active Learning
Ma, X., Ma, X., Erfani, S. M. & Bailey, J., 2026, In: Transactions on Machine Learning Research. 2026-May, 22 p.Research output: Contribution to journal › Article › Research › peer-review