Machine Learning with Impact

Professor Fang Chen

Abstract:

In the digital era, big data analytics helps governments and industries to harness the power of data – more efficient operations, more cost savings, higher profits and happier customers. Moreover, it can rapidly revolutionise traditional solutions and ways of thinking in the industry for real-world applications and impact.

 Machine Learning (ML) discovers patterns from discriminated data and builds predictive capability from the derived patterns. They are widely used in many areas, such as the financial market and search engines, and there is fast-growing demand for infrastructure, transport, smart city, agriculture and more. The impact of ML is in utilising data to gain unique business insights and in providing innovative solutions for better productivity, safety and community benefit. This talk shares insights into how to create innovative ML solutions with huge versatility and global impact.

Distinguished Professor Fang Chen is an award-winning, internationally-recognised leader in artificial intelligence (AI) and data science. She is passionately innovative in her work, architecting and implementing data-driven solutions to problems met in governments and industries. Her experience in solving these real-life complex problems in large-scale networks span across transport, water, energy and agriculture. Fang is also actively involved in promoting ethical, human-centred AI.

In terms of personal recognition, Fang is the winner of the “Oscar” of Australian science – the Australian Museum Eureka Prize 2018 for Excellence in Data Science. She is the 2021 winner of the Australia and New Zealand “Women in AI” Award in Infrastructure, and a 2021 winner of the NSW Premier’s Prize of Science and Engineering. She is also the Australian Water Association’s “Water Professional of the Year”, awarded in 2016.

Through impactful successes, the multidisciplinary team she led has won major industry awards on the national level such as the ITS Australia National Awards in 2014, 2015 and 2018; NSW iAwards 2017; VIC iAwards 2019 and 2020; and the National Award and NSW “Research and Innovation Award” 2018 and 2022 from the Australian Water Association.

Fang is a member of the inaugural NSW Government AI Advisory Committee. She also serves on the expert panel of the Singapore National Science Foundation, and on several boards, including ITS Australia. She has 350 publications and 30 patents in 8 countries. Currently, Fang is the Executive Director of Data Science at the University of Technology Sydney (UTS) and the Executive Director of the UTS Data Science Institute.

Exploring Online Social Networks: Issues and Approaches

Professor Jeffrey Xu Yu

Abstract:

Social networks have been studied since 1890s to study social ties or relationships among social entities, which can be individuals, communities, etc. With rapid growth of WWW, social networks online such as Facebook, Twitter, etc, it becomes possible to study social networks over such rich datasets in size that cannot be easily studied in the past. Online social networks have been extensively studied over decades to understand large complex social networks online using data/graph analytics. To better understand online social networks, graph algorithms and graph systems have played a very important rule. In this talk, we will discuss some selected research topics for online social networks from graph algorithm perspectives. The topics include but not limited to social communities such as overlapping communities, influential communities, skyline communities, as well as triangle-free densest structure, finding critical users in social communities, and finding social hierarchy.

Dr Jeffrey Xu Yu is a Professor in the Department of Systems Engineering and Engineering Management, The Chinese University of Hong Kong. His current main research interests include graph algorithms, graph processing systems, and query processing in database systems. Dr. Yu served as an Information Director and a member in ACM SIGMOD executive committee (2007-2011), an associate editor of IEEE TKDE (2004-2008), and an associate editor in VLDB Journal (2007-2013). Currently he serves as an associate editor of ACM TODS, WWW Journal, Data Science and Engineering, the International Journal of Cooperative Information Systems, the Journal on Health Information Science and Systems (HISS), and Journal of Information Processing. Dr. Yu served/serves in many organization committees and program committees in international conferences/workshops including PC Co-chair of APWeb’04, WAIM’06, APWeb/WAIM’07, WISE’09, PAKDD’10, DASFAA’11, ICDM’12, NDBC’13, ADMA’14, CIKM’15, Bigcomp17, DSAA’19, CIKM’19, and DASFAA’20, and conference general Co-chair of APWeb’13, ICDM’18, and ADC’22.