Special Sessions

Let’s talk about uncertainty

Abstract

Statistical and machine learning analyses provide a wide array of insights to expand knowledge and enhance data-focused decision making. This has led to a surge in demand for, and indeed understanding of, estimates and predictions based on these analyses, not only by scientists but also policy makers and the public. A concomitant issue that is perhaps less well addressed or understood is the uncertainty of these values. In this presentation, I will touch on the many forms of uncertainty in statistical and machine learning analyses, and discuss by example some of our approaches to quantification and communication of uncertainty. The argument will be made that while we have come a long way, the expression and use of uncertainty in science and decision-making is still in its infancy. Indeed, I will posit that SML uncertainty is so important for all members of the community that it could be considered the ‘4th R’ after reading, writing and arithmetic. I will preface the talk with a brief discussion about women in data science, and I will mention our ambition at QUT to create a Centre of Excellence to amplify our research. 

Bio

Kerrie Mengersen graduated in 1985 with a Bachelor of Arts (Honours Class 1), majoring in Mathematics (Statistics) and Computing, and received her PhD in Mathematical Statistics in 1989 from the University of New England, New South Wales. Her PhD thesis was on the topic of ranking and selection under the supervision of Professor Eve Bofinger, one of the pioneer female university researchers in regional Australia.

Following graduation, she was recruited to a commercial statistical consulting company, which provided her with strong experience in a wide range of statistical methods in the context of diverse applied problems. Her career since then has been characterised by a dual focus of engaging with and developing new statistical methodology motivated by, and motivating, challenging statistical applications.

In 2016, QUT awarded the title of Distinguished Professor to Professor Kerrie Mengersen in recognition of her outstanding achievements, both nationally and internationally, in mathematics and statistical research. Distinguished Professor Mengersen is acknowledged to be one of the leading researchers in her discipline.

In 2016 Professor Mengersen also received two more prestigious awards: the Statistical Society of
Australia’s Pitman Medal, the highest award presented by the Society and the first time it has been presented to a woman, and the Research Excellence award by the Cooperative Research Centre for Spatial Analysis (CRCSI).

In 2018 Professor Mengersen was elected a Fellow of the Australian Academy of Science (AAS);  a Fellow of the Academy of Social Sciences in Australia (ASSA); and an Invited Fellow of the Queensland Academy of Arts and Sciences (QAAS)

Managing and Mining with Massive Graphs

Abstract

In this talk, I will share some of my research on massive graph data analysis and processing that can be applied to many real scenarios in industries. The topics include personalized PageRank query processing and graph embedding.

Bio

Sibo Wang is an Assistant Professor in the Department of Systems Engineering and Engineering Management at The Chinese University of Hong Kong since Dec 2018. He received his B.E. in Software Engineering in 2011 from Fudan University and his Ph.D. in Computer Science in 2016 from Nanyang Technological University. His main research area is database and data mining, especially big data analytics and processing, graph mining and graph representation learning. Most of his research works have been published in top conferences like SIGMOD, VLDB, and SIGKDD. His professional services include Workshop Chair at ICDE 2022 Conference and Local Organization Chair of ADC 2018, Program Committee for VLDB 2020-2021, SIGKDD 2019-2021, WWW2020-2022, ICDE 2021-2022, IJCAI 2020, AAAI 2021-2022, PAKDD 2018-2022, DASFAA 2019-2022. He also served as a peer reviewer in journals like TODS, TKDE, VLDBJ, TOIS, and TKDD.