Kok-Leong Ong is a Professor of Business Analytics in the College of Business & Law, RMIT University. He is currently working on a range of analytics projects making data actionable through analytics-2-business translation, automation, and applications.
Kok-Leong Ong is currently a Professor of Business Analytics in the College of Business & Law, RMIT University.
His research focuses on analytics and machine learning translation into practice within different business verticals, and the development of new techniques as required to meet individual business needs. As a result, Kok-Leong's research has had a strong focus on impact, where many of his works were ranked by Altmetric to be in the top 25% and 5% of research in their respective domains. He has received over $1.4m in research grants and serves in various conferences related to analytics, including the prestigious KDD and PAKDD.
Kok-Leong is also well-known for his strategic leadership in programs. He was one of the members that started Australia's first degree in Business Analytics and led the development of La Trobe Business School's foray into Business Analytics from 2015 to 2019. From 2020, he contributed to the Business School's digital transformation program before joining RMIT in September 2021. He has won several teaching and service awards, including two VC's Teaching Award. In 2021, he was one of the four Australia-based academics to be named the Leading Data Academics by CDO Magazine.
- Ph.D., Nanyang Technological University, Singapore
- Thesis: Mining frequent patterns for effective business objectives
- Pourhabibi, T.,Ong, K.,Kam, B.,Boo, Y. (2021). DarkNetExplorer (DNE): Exploring dark multi-layer networks beyond the resolution limit In: Decision Support Systems, 146, 1 - 15
- Pourhabibi, T.,Ong, K.,Boo, Y.,Kam, B. (2021). Detecting covert communities in multi-layer networks: A network embedding approach In: Future Generation Computer Systems, 124, 467 - 479
- Li, T.,Wu, Y.,Wu, F.,Mohammed, S.,Wong, R.,Ong, K. (2021). Sleep pattern inference using IoT sonar monitoring and machine learning with Kennard-stone balance algorithm In: Computers and Electrical Engineering, 93, 1 - 19
- Li, J.,Wu, Y.,Fong, S.,Wong, R.,Chu, V.,Ong, K.,Wong, K. (2021). Dynamic swarm class rebalancing for the process mining of rare events In: Journal of Supercomputing, 77, 7549 - 7583
- Stirling, E.,Willcox, J.,Ong, K.,Forsyth, A. (2021). Social media analytics in nutrition research: A rapid review of current usage in investigation of dietary behaviours In: Public Health Nutrition, 24, 1193 - 1209
- Laws, R.,Love, P.,Hesketh, K.,Ong, K-L., et al, . (2021). Protocol for an Effectiveness-Implementation Hybrid Trial to Evaluate Scale up of an Evidence-Based Intervention Addressing Lifestyle Behaviours From the Start of Life: INFANT In: Frontiers in Endocrinology, 12, 1 - 11
- Pourhabibi, T.,Ong, K.,Kam, B.,Boo, Y. (2020). Fraud detection: A systematic literature review of graph-based anomaly detection approaches In: Decision Support Systems, 133, 1 - 15
- Pourhabibi, T.,Boo, Y.,Ong, K.,Kam, B.,Zhang, X. (2019). Behavioral Analysis of Users for Spammer Detection in a Multiplex Social Network In: Proceedings of the 16th Australasian Conference (AusDM 2018), Bathurst, NSW, Australia, 28-30 November 2018
- Carey, L.,Walsh, A.,Adikari, A.,Goodin, P.,Alahakoon, D.,De Silva, D.,Ong, K.,Nilsson, M.,Boyd, L. (2019). Finding the Intersection of Neuroplasticity, Stroke Recovery, and Learning: Scope and Contributions to Stroke Rehabilitation In: Neural Plasticity, 2019, 1 - 16
- Taki, S.,Russell, C.,Lymer, S.,Laws, R.,Campbell, K.,Appleton, J.,Ong, K.,Denney-Wilson, E. (2019). A mixed methods study to explore the effects of program design elements and participant characteristics on parents' engagement with an mHealth program to promote healthy infant feeding: The growing healthy program In: Frontiers in Endocrinology, 10, 1 - 13
- Evaluating real-world implementation of an evidence-based program addressing lifestyle behaviours from the start (administered by Deakin University). Funded by: NHMRC Partnership Project Grant via other University from (2022 to 2024)
- Infant2Child: Optimising nutrition in early life to reduce childhood dental caries (administered by Murdoch Children's Research Institute). Funded by: MRFF - PPHR Initiative - 2020 Maternal First 2000 Days and Childhood Health from (2021 to 2026)
3 PhD Current Supervisions and 2 Masters by Research Current Supervisions