Kate Smith-Miles is an ARC Australian Laureate Fellow, and Professor in the School of Mathematics and Statistics at The University of Melbourne. She was Head of School at Monash University from 2009-2014. She is currently President of the Australian Mathematical Society. She is also the inaugural Director of MAXIMA (the Monash Academy for Cross & Interdisciplinary Mathematical Applications). Her research focuses on optimisation, machine learning, time series analysis, and applications of applied mathematics to tackle interdisciplinary and industrial problems. She was awarded the Australian Mathematical Society Medal in 2010 for distinguished research, and the EO Tuck Medal from ANZIAM in 2017 for outstanding research and distinguished service to applied mathematics. She serves on the ARC College of Experts, and Chairs the Advisory Board for the AMSI Choose Maths program aiming to encourage greater participation of women and girls in mathematics.
Water-quality sensors are exposed to changing environments and extreme weather conditions and thus are prone to errors, including failure. These technical errors make data unreliable and untrustworthy and affect performance of any subsequent data analysis. ACEMS researchers, led by Priyanga Dilini Talagala, have proposed a feature based procedure, named oddwater, for detecting technical outliers in water-quality data derived from in situ sensors.
ACEMS researchers examine how accurate the methods are for measuring landscape characteristics on optimisation black-box problems. Their work also provided a methodology and curated data that can be used by other researchers to identify robust measuring methods.
The ability to detect anomalies or outliers in time series, and to develop algorithms that can find them at the earliest possible sign of a deviation from expected behaviour, is critical for many applications including those arising in security, epidemiology, and monitoring of critical infrastructure. This project aims to develop new methods to characterise the expected time series behaviour of systems, even in the presence of significant noise and concept drift, and with spatio-temporal and multivariate time series.
Anomalies can be the main carriers of significant and often critical information, and the identification of anomalies is a key task in many fields such as cyber security, intrusion detection, water quality monitoring, system health monitoring, environmental monitoring. ACEMS researchers, led by Priyanga Dilini Talagala, have proposed a framework that provides early detection of anomalous series within a large collection of streaming time-series data.
There are about 50,000 bushfires every year in Australia, according to a 2009 report by the Australian Institute of Criminology.
For many Australians, this number is probably not that surprising. What is more surprising, though, is that mathematicians may have a key role to play in fighting these fires.
One of these fire-fighting mathematicians is Dr Sevvandi Kandanaarachchi – a postdoctoral fellow and Associate Investigator at ACEMS. She works closely with Professor Kate Smith- Miles, one of ACEMS’ Chief Investigators.
Steponavič\.e, I., Shirazi-Manesh M., Hyndman R. J., Smith-Miles K., Villanova L., Pardalos P.. M., et al.
(2016). On sampling methods for costly multi-objective black-box optimization. Advances in Stochastic and Deterministic Global Optimization. 273–296.
Invited talks, refereed proceedings and other conference outputs
(2019). Instance Spaces for Objective Assessment of Algorithms and Benchmark Test Suites. 1st Workshop on Evaluation and Experimental Design in Data Mining and Machine Learning (EDML 2019) Workshop at the SIAM International Conference on Data Mining (SDM19), May 2‑4, 2019.
Acosta, M. Munoz, Hyndman R. J., & Smith-Miles K.
(2018). About outlier detection. AustMS 2018.
Edwards, S.. J., Baatar D.., Ernst A.. T., & Smith-Miles K.
(2018). The Liquid Handling Robot Scheduling. SPARK-18. Proceedings of 11th Workshop on Scheduling and Planning Applications Workshop, pp 18-26.
(2018). Optimization in the Darkness of Uncertainty: when you don't know what you don't know, and what you do know isn't much!. ANZIAM.
(2018). Instance Spaces for Objective Assessment of Algorithms and Benchmark Test Suites. International Joint Conference on Artificial Intelligence, Workshop of Data Science Meets Optimisation.
Edwards, S. J., Baatar D., Ernst A., & Smith-Miles K.
(2018). The Liquid Handling Robot Scheduling Problem. International Conference of Austomated Planning and Scheduling (ICAPS): Scheduling and Planning Applications woRKshop (SPARK). 18-26.
Edwards, S. J., Baatar D., Bowly S., & Smith-Miles K.
(2018). Symmetry breaking in a special case of the RCPSP/max. Multidisciplinary International Conference on Scheduling: Theory and Applications (MISTA). 315-318.
Steponavič\.e, I., Hyndman R. J., Smith-Miles K., & Villanova L.
(2017). Dynamic Algorithm Selection for Pareto Optimal Set Approximation. Journal of Global Optimization. 67, 263-282. doi: 10.1007/s10898-016-0420-x