I received the B.Eng. and M.Eng. degrees in Electronics Engineering from Universidad del Valle, Colombia, in 2005 and 2008, respectively, and the Ph.D. degree in Engineering from The University of Melbourne, in 2014. From 2014 to 2017, I was a Research Fellow at the School of Mathematical Sciences, Monash University. Currently, I am a Research Fellow at the School of Mathematics and Statistics, The University of Melbourne.
My research focuses on the application of optimisation, computational intelligence, signal processing, data analysis, and machine learning methods to ill-defined science, engineering and medicine problems. My current work focuses on the algorithm selection problem for black-box optimisation (both single- and multi-objective) and machine learning problems, anomaly detection on streaming data, minerals criticality, industrial sustainability performance measurement, and gait analysis.
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.
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.
Invited talks, refereed proceedings and other conference outputs