ARC-Funded Research Projects
Published:
LP160101885 – Intruder alert! Detecting and Classifying Events in Noisy Time Series (2017)
| Investigators: K.A. Smith-Miles, R. Hyndman, M.A. Muñoz, J. Katsifolis | Funding: AU$204,000 (ARC Linkage) |
Developed robust methods to detect and classify intrusion events in noisy, multi-dimensional time series under diverse operating environments, while ignoring nuisance events. Applied to fibre optic sensing systems in collaboration with Future Fibre Technologies Ltd.
Publications: J24, J31, J32 (see Publications page)
RC53128 – Improved Intrusion Detection Analysis (2016)
| Investigators: K.A. Smith-Miles, M.A. Muñoz, S. Kandanaarachchi | Funding: AU$100,000 (Dept. of Industry Research Connections) |
Extended and industrialised the anomaly detection methods from the Laureate program for deployment in real intrusion detection systems.
RC48547 – New Mathematical Models for Data Handling (2015)
| Investigators: K.A. Smith-Miles, M.A. Muñoz | Funding: AU$100,000 (Dept. of Industry Research Connections) |
Developed foundational mathematical models for handling high-dimensional, non-stationary data streams, forming the basis for subsequent algorithm selection research.
ACEMS Research Sprint – Exploring Strategies for Constructing Algorithm Portfolios (2021)
| Investigators: M.A. Muñoz, S. Kandanaarachchi | Funding: AU$20,000 (ARC Centre of Excellence ACEMS) |
Developed methodology for assessing algorithm portfolio performance in terms of risk and robustness, contributing to principled portfolio construction.
IG240100160 – A Scalable Machine Learning Platform for Pharmaceutical Manufacture (2025)
| Investigators: M. Czyz, A. Polyzos, K. Smith-Miles, M.A. Muñoz | Funding: AU$428,296 (Australia Economic Accelerator Ignite) |
Development of scalable machine learning tools to optimise pharmaceutical manufacturing processes.