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.
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.
The Ren Potts Medal of the Australian Society for Operations research is intended to recognise individuals who have made outstanding contributions to theory or practice of OR in Australia. It is a national award.
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
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