Professor James Bailey

  • Room: Level: 07 Room: 7.09
  • Building: Doug McDonell Building
  • Campus: Parkville

Research interests

  • Artificial intelligence
  • Data mining
  • Health informatics
  • Immersive Simulation
  • Learning Analytics
  • Machine learning

Personal webpage


James Bailey is a Professor in the School of Computing and Information Systems, The University of Melbourne, and has been an Australian Research Council Future Fellow. He is a researcher and educator in machine learning, artificial intelligence and data science.

Recent publications

  1. Mirmomeni, M.; Kowsar, Y.; Kulik, L.; Bailey, J. An automated matrix profile for mining consecutive repeats in time series. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer Nature. 2018, Vol. 11013 LNAI, pp. 192-200. DOI: 10.1007/978-3-319-97310-4_22
  2. Ma, X.; Li, B.; Wang, Y.; M. Erfani, S.; Wijewickrema, S.; Schoenebeck, G.; Song, D.; Houle, ME.; Bailey, J. Characterizing Adversarial Subspaces Using Local Intrinsic Dimensionality. . ICLR. 2018.
  3. Wijewickrema, S.; Copson, B.; Ma, X.; Briggs, R.; Bailey, J.; Kennedy, G.; Oleary, S. Development and Validation of a Virtual Reality Tutor to Teach Clinically Oriented Surgical Anatomy of the Ear. Proceedings - IEEE Symposium on Computer-Based Medical Systems. IEEE. 2018, Vol. 2018-June, pp. 12-17. DOI: 10.1109/CBMS.2018.00010
  4. Ma, X.; Wang, Y.; Houle, ME.; Zhou, S.; Erfani, SM.; Xia, ST.; Wijewickrema, S.; Bailey, J. Dimensionality-Driven learning with noisy labels. 35th International Conference on Machine Learning, ICML 2018. JMLR. 2018, Vol. 8, pp. 5332-5341.
  5. Zhou, Y.; Wijewickrema, S.; Ioannou, I.; Bailey, J.; Kennedy, G.; Nestel, D.; O'leary, S. Do experts practice what they profess?. PLOS ONE. PUBLIC LIBRARY SCIENCE. 2018, Vol. 13, Issue 1. DOI: 10.1371/journal.pone.0190611
  6. Ganji, M.; Chan, J.; Stuckey, PJ.; Bailey, J.; Leckie, C.; Ramamohanarao, K.; Davidson, I. Image constrained blockmodelling: A constraint programming approach. SIAM International Conference on Data Mining, SDM 2018. Society for Industrial and Applied Mathematics. 2018, pp. 19-27.
  7. Wang, Y.; Liu, W.; Ma, X.; Bailey, J.; Hongyuan, Z.; Song, L.; Xia, S-T. Iterative Learning with Open-set Noisy Labels. . The Computer Vision Foundation. 2018.
  8. Bellomo, R.; Chan, M.; Guy, C.; Proimos, H.; Franceschi, F.; Crisman, M.; Nadkarni, A.; Ancona, P.; Pan, K.; Di Muzio, F.; Presello, B.; Bailey, J.; Young, M.; Hart, GK. Laboratory alerts to guide early intensive care team review in surgical patients: A feasibility, safety, and efficacy pilot randomized controlled trial. Resuscitation. ELSEVIER IRELAND LTD. 2018, Vol. 133, pp. 167-172. DOI: 10.1016/j.resuscitation.2018.10.012
  9. Wang, Y.; Dai, B.; Kong, L.; Erfani, SM.; Bailey, J.; Zha, H. Learning deep hidden nonlinear dynamics from aggregate data. 34th Conference on Uncertainty in Artificial Intelligence 2018, UAI 2018. 2018, Vol. 1, pp. 83-92.
  10. Moshtaghi, M.; Bezdek, JC.; Erfani, SM.; Leckie, C.; Bailey, J. Online cluster validity indices for performance monitoring of streaming data clustering. International Journal of Intelligent Systems. 2018. DOI: 10.1002/int.22064
  11. Wijewickrema, S.; Zhou, Y.; Ioannou, I.; Copson, B.; Piromchai, P.; Yu, C.; Briggs, R.; Bailey, J.; Kennedy, G.; O'leary, S. Presentation of automated procedural guidance in surgical simulation: Results of two randomised controlled trials. The Journal of Laryngology & Otology. 2018, Vol. 132, Issue 3, pp. 257-263. DOI: 10.1017/S0022215117002626
  12. Wijewickrema, S.; Ma, X.; Piromchai, P.; Briggs, R.; Bailey, J.; Kennedy, G.; O Leary, S. Providing automated real-time technical feedback for virtual reality based surgical training: Is the simpler the better?. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer International Publishing. 2018, Vol. 10947 LNAI, pp. 584-598. DOI: 10.1007/978-3-319-93843-1_43
  13. Zhao, Y.; Calheiros, R.; Gange, G.; Bailey, J.; Sinnott, R. SLA-Based Profit Optimization Resource Scheduling for Big Data Analytics-as-a-Service Platforms in Cloud Computing Environments. IEEE Transactions on Cloud Computing. 2018. DOI: 10.1109/TCC.2018.2889956
  14. Romano, S.; Vinh, NX.; Verspoor, K.; Bailey, J. The randomized information coefficient: assessing dependencies in noisy data. Machine Learning. SPRINGER. 2018, Vol. 107, Issue 3, pp. 509-549. DOI: 10.1007/s10994-017-5664-2
  15. Amsaleg, L.; Bailey, J.; Barbe, D.; Erfani, S.; Houle, ME.; Nguyen, V.; Radovanovic, M. The vulnerability of learning to adversarial perturbation increases with intrinsic dimensionality. 2017 IEEE Workshop on Information Forensics and Security, WIFS 2017. IEEE Explore. 2018, Vol. 2018-January, pp. 1-6. DOI: 10.1109/WIFS.2017.8267651
  16. Lederman, R.; Constantinidis, D.; Linden, T.; Corrin, L.; Smith, W.; Pearce, J.; Bailey, J. Using a Traffic Light System to Provide Feedback to IS Masters Students. ICIS 2017: Transforming Society with Digital Innovation. AISel. 2018.
  17. Ganji, M.; Bailey, J.; Stuckey, PJ. A declarative approach to constrained community detection. Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics). Springer. 2017, Vol. 10416 LNCS, pp. 477-494. DOI: 10.1007/978-3-319-66158-2_31
  18. Naderivesal, S.; Kulik, L.; Bailey, J. A fast and accurate index structure for spatiotemporal trajectories. ACM International Conference Proceeding Series. Association for Computing Machinery (ACM). 2017, pp. 403-411. DOI: 10.1145/3144457.3144497
  19. Ma, X.; Wijewickrema, S.; Zhou, S.; Zhou, Y.; Mhammedi, Z.; O'leary, S.; Bailey, J. Adversarial generation of real-time feedback with neural networks for Simulation-based training. IJCAI International Joint Conference on Artificial Intelligence. International Joint Conferences on Artificial Intelligence. 2017, pp. 3763-3769.
  20. Naghizade, E.; Bailey, J.; Kulik, L.; Tanin, E. Challenges of differentially private release of data under an open-world assumption. ACM International Conference Proceeding Series. ACM New York. 2017, Vol. Part F128636. DOI: 10.1145/3085504.3085531

View a full list of publications on the University of Melbourne’s ‘Find An Expert’ profile