Keynote Speakers

As is tradition for AusDM we have lined up an excellent keynote speaker program. Each speaker is a well-known research and/or practitioner in data mining and related disciplines. The keynote program provides an opportunity to hear from some of the world’s leaders on what the technology offers and where it is heading.

Date: Friday 4th December 2020

Agenda:
1-2pm AEDT: Keynote Talk 1
2-3pm AEDT: Keynote Talk 2

Chair: Richi Nayak

Talk 1: Irrational Exuberance: Has Deep Learning Contributed anything to Time Series Data Mining?

Slides: http://bit.ly/3lyVMwZ

Speaker: Dr. Eamonn Keogh

Abstract – Time series data mining has been a perennially important sub-topic in data mining for three decades. In the last six years or so, Deep Learning has been the hottest general data mining/machine learning methodology. Not surprisingly, there are now many hundreds of papers applying Deep Learning to time series data mining problems.

In this talk I will make a surprising claim. There is actually very little evidence that deep learning has contributed anything to time series mining/machine learning. Almost all the apparent success can be attributed to one or more common errors, including (presumably inadvertent) crippling of rival methods, cherry-picking of datasets etc. In this talk I will demonstrate these claims with novel experiments and demonstrations.

Bio: Dr. Eamonn Keogh is a full professor at the University of California Riverside. He has invented many commonly used data mining primitive and representations, including Symbolic Aggregate approXimation (SAX), Piecewise Aggregate Approximation (PAA), Time Series Motifs, Time Series Shapelets, Time Series Discords and most recently, The Matrix Profile. He is the most prolific author in the Data Mining and Knowledge Discovery Journal, and a top ten most prolific author in ACM SIGKDD, IEEE ICDM, and SIAM SDM. He has won at least one best paper award at each of ACM SIGMOD, ACM SIGKDD, IEEE ICDM, and SIAM SDM.

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Talk 2: Extraction of Hidden Information from Social Media

Speaker: Bela Stantic

AbstractIn this talk Prof Stantic will demonstrate the power of Big Data analytics through elaborating diverse areas of existing projects in the Big Data and Smart Analytics lab, ranging from sentiment in Tourism to predicting environmental changes. Using examples Professor Stantic will show how social media data can be mined to extract valuable information hidden deeply in publicly available data.

Bio: Bela Stantic is a Professor in Data Science and founder and Director of “Big Data and Smart Analytics” Lab at Griffith University. Professor Stantic is internationally recognized in the field of data analytics and efficient management of complex data structures, such as found in Big Data. He was invited to give many keynotes and invited talks at highly ranked International conferences and prestigious institutions. He successfully applied his research interdisciplinary, most prominently applying Big Data analytics to the environment, tourism, as well as to health domain. He has published more than 160 journals and conference peer-reviewed publications, which in turn helped to attract competitive funding from different sources. He was also being involved in the supervision of more than 25 PhD students.