STAFF PROFILE
Associate Professor Jeffrey Chan
Position:
Associate Professor
College / Portfolio:
STEM College
School / Department:
STEM|School of Computing Technologies
Phone:
+61399255270
Email:
jeffrey.chan@rmit.edu.au
Campus:
City Campus
Contact me about:
Research supervision
Jeffrey is a senior lecturer in the School of Computing Technologies. His research interests lie in machine learning, social network analysis, recommender systems, FAT (Fairness, Accountable, Transparent) machine learning, data driven optimisation and interdisciplinary research that combines these fields to solve social and industry based applications. He has worked with industry and non-profit partners in retail, sustainability, energy, manufacturing and health.
PhD, University of Melbourne
- Sarwar, T.,Seifollahi, S.,Chan, J.,Zhang, X.,Aksakalli, V.,Hudson, I.,Verspoor, C.,Cavedon, L. (2022). The Secondary Use of Electronic Health Records for Data Mining: Data Characteristics and Challenges In: ACM Computing Surveys, 55, 1 - 36
- Zhang, S.,Zhang, J.,Lau, J.,Chan, J.,Paris, C. (2021). Less Is More: Rejecting Unreliable Reviews for Product Question Answering In: Machine Learning and Knowledge Discovery in Databases. ECML PKDD 2020. Lecture Notes in Computer Science, vol 12459, Ghent, Belgium, 14-18 September 2020
- Almusallam, N.,Tari, Z.,Chan, J.,Al-Harthi, A.,Alabdulatif, A.,Al-Naeem, M. (2021). Towards an Unsupervised Feature Selection Method for Effective Dynamic Features In: IEEE Access, 9, 77149 - 77163
- Sultana, N.,Chan, J.,Sarwar, T.,Qin, K. (2021). Learning to Optimise Routing Problems using Policy Optimisation In: Proceedings of the 2021 International Joint Conference on Neural Networks (IJCNN 2021), Shenzhen, China, 18-22 July 2021
- Taylor, K.,Ha, H.,Li, M.,Chan, J.,Li, X. (2021). Bayesian Preference Learning for Interactive Multi-objective Optimisation In: Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2021), Lille, France, 10 -14 July 2021
- Saiedur Rahaman, M.,Shao, W.,Salim, F.,Turky, A.,Song, A.,Chan, J.,Jiang, J.,Bradbrook, D. (2021). MoParkeR: Multi-objective Parking Recommendation In: Proceedings of the 33rd International Conference on Scientific and Statistical Database Management (SSDBM 2021), Tampa, Florida, United States, 6-7July 2021
- Halder, S.,Lim, K.,Chan, J.,Zhang, X. (2021). Transformer-based multi-task learning for queuing time aware next POI recommendation In: Proceedings of the 25th Pacific-Asia Conference on Advances in Knowledge Discovery and Data Mining (PAKDD 2021), Online, 11-14 May 2021
- Sun, Y.,Wang, W.,Kirley, M.,Li, X.,Chan, J. (2020). Revisiting Probability Distribution Assumptions for Information Theoretic Feature Selection In: Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020), New York, United States, 7–12 February 2020
- Demirovic, E.,Stuckey, P.,Bailey, J.,Chan, J.,Leckie, C.,Ramamohanarao, K.,Guns, T. (2020). Dynamic Programming for Predict+ Optimise In: Proceedings of the Thirty-Fourth AAAI Conference on Artificial Intelligence (AAAI 2020), New York, United States, 7–12 February 2020
- Chen, X.,Zhou, X.,Chan, J.,Chen, L.,Sellis, T.,Zhang, Y. (2020). Event Popularity Prediction Using Influential Hashtags from Social Media In: IEEE Transactions on Knowledge and Data Engineering, , 1 - 15
Note: Supervision projects since 2004
13 PhD Current Supervisions6 PhD Completions
Machine learning, Data mining, Clustering, Matrix factorisation, Social network analysis, Graph mining, Graph similarities, Graph modelling, Combinatorial optimisation, Dimension reduction
- Machine learning techniques for fuel loss detection at service stations. Funded by: ARC Linkage Project Grants 2019 from (2021 to 2024)
- Learning the Focus of Attention to Detect Distributed Coordinated Attacks (externally led by University of Melbourne). Funded by: ARC Discovery Projects via other university 2020 from (2020 to 2023)
- Archistar Innovation Connection Grant. Funded by: Archistar Pty Ltd Contract from (2019 to 2020)
- Rygbee Campus. Funded by: Rygbee USA - Scholarship from (2018 to 2022)
- Optimisation and machine learning for wetstock management. Funded by: DIIS - Innovations Connections - Competitive from (2018 to 2019)