| 21 Aug: Big Data Forum (Meeting room 103, MCEC) |
| Time |
Talk |
Speaker |
| 08:30-09:00 |
Registration |
|
| 09:00-09:10 |
Opening |
Prof Longbing Cao |
| 09:10-09:35 |
Invited Talk: Applications of Big Data Techniques for Social Analytics
Dr Dickson Lukose 
Chief Data Scientist
GCS Agile, Australia
With over 25 years of experience in consulting services, applied research and development of Intelligence Systems, Dr. Lukose now leads a team of Data Scientist doing Big Data Analytics (BDA). Prior to joining GCS Agile, Dr. Lukose was the Senior Director of the Artificial Intelligence (AI) Lab in MIMOS Berhad (Malaysia) developing AI software for BDA. Earlier in his career, Dr. Lukose worked as Principal Knowledge Engineer with Mindbox Inc. (USA).
|
 |
| 09:35-10:00 |
Invited Talk
Prof James Bailey
The University of Melbourne
James Bailey is a Professor in School of Computing and Information Systems at University of Melbourne. He has served in editorial boards of Knowledge and Information Systems, Social Network Analysis and Mining, and IEEE Transactions on Knowledge and Data Engineering (2011-2015). He also served as co-chair for PAKDD 2016, CIKM 2015, tutorial co-chair of ICDM 2014. His research interests include machine learning and data mining, Big Data, learning analytics. He has continually obtained ARC grants on data mining projects since 2011.
|
 |
| 10:00-10:20 |
Morning Tea |
|
| 10:20-11:20 |
Keynote speech: The Past, Present, and Future of Artificial Intelligence
The talk will cover the historical development of AI, current and future trends, applications, opportunities for industry in the near and medium term, and likely benefits. Looking further ahead: I will ask whether human-level AI is achievable, and, if so, what are the likely impacts on society. Are there risks, and how could they arise?
I will suggest a fundamental reorientation of the field of AI towards provably beneficial systems and will outline methods for designing such systems.
Prof Stuart Russell
University of California, Berkeley
Stuart Russell received his B.A. with first-class honours in physics from Oxford University in 1982 and his Ph.D. in computer science from Stanford in 1986. He then joined the faculty of the University of California at Berkeley, where he is Professor (and formerly Chair) of Electrical Engineering and Computer Sciences and holder of the Smith-Zadeh Chair in Engineering. He has served as an Adjunct Professor of Neurological Surgery at UC San Francisco and as Vice-Chair of the World Economic Forum’s Council on AI and Robotics. He is a recipient of the Presidential Young Investigator Award of the National Science Foundation, the IJCAI Computers and Thought Award, the World Technology Award (Policy category), the Mitchell Prize of the American Statistical Association, and Outstanding Educator Awards from both ACM and AAAI. From 2012 to 2014 he held the Chaire Blaise Pascal in Paris. He is a Fellow of the American Association for Artificial Intelligence, the Association for Computing Machinery, and the American Association for the Advancement of Science. His book “Artificial Intelligence: A Modern Approach” (with Peter Norvig) is the standard text in AI; it has been translated into 13 languages and is used in over 1300 universities in 118 countries. His research covers a wide range of topics in artificial intelligence including machine learning, probabilistic reasoning, knowledge representation, planning, real-time decision making, multitarget tracking, computer vision, computational physiology, and philosophical foundations. He also works for the United Nations, developing a new global seismic monitoring system for the nuclear-test-ban treaty. His current concerns include the threat of autonomous weapons and the long-term future of artificial intelligence and its relation to humanity.
|
 |
| 11:20-11:45 |
Adoption of AI / Machine Learning in IP Australia
Dramatic improvements in machine learning, computer vision, natural language processing, speech recognition, and robotics have driven the rise of AI technology. Combining these technologies means that some tasks that traditionally required human involvement can now be completely undertaken by machines, while in others, human efforts can be greatly augmented. In the context of the Australian government, AI technologies are especially attractive due to their ability to tackle common challenges including but not limited to resource constraints and process heavy operations, responsiveness of government organisations, and fact-based, proactive decision making.
IP Australia is undertaking AI research and development activities, and in July 2017 released an early public prototype called Trade Mark Assist. Leveraging artificial intelligence capabilities including machine learning and natural language processing, Trade Mark Assist educates and assists self-filers, in particular small and medium enterprises, through the initial stages of the trade mark application process. The presentation aims to provide, at a high-level, the vision of IP Australia related to AI/Machine Learning, introduction of Trade Mark Assist with a particular focus on machine learning, and challenges and lessons learnt specific to applying AI initiatives in the government context. Dr Kyusik Kim
Director Cognitive Futures
IP, Australia
Kyusik is leading AI/Machine Learning research and development within IP Australia. His section is developing prototypes to demonstrate how Machine Learning can improve the quality and timeliness of IP rights processing, draw insights from new and existing data, and provide a citizen-centric 24/7 experience to IP Australia’s customers.
Prior to joining IP Australia, Kyusik served as senior team leader at a tier 1 management consulting firm and worked with clients from Federal Government, Telecommunication, Finance, and Automotive industries. Kyusik holds a PhD (ANU), Masters (ANU), and Bachelor (USYD) degree in Business Information System. Kyusik’s main research areas of interest include; machine learning, IT & productivity, and Big Data.
|
 |
| 11:45-12:10 |
Invited Talk: Data Discovery: The Search for the Light at the End of a Tunnel
This presentation is a cautionary tale about delivering business outcomes using the tools and techniques of data science.
Traditional data science is about using structured data and known examples to develop models to detect cases of interest in data. Signature detection and anomaly detection are commonly used for this purpose. Another challenge in data science is discovering patterns, trends and relationships in data. The former is known as the supervised or guided-learning tradition and the latter the unsupervised or the discovery-learning practice.
This presentation provides a ‘war story’ of one team’s quest to discover knowledge and insights in data. It provides some salutary lessons about going down familiar and well-trodden paths to try to make discoveries only to find that the team was moving in the wrong direction and in some cases heading down dead-end alleys. The presentation covers how the team got back on track in trying to find the elusive light at the end of a tunnel. Dr Warwick Graco
ATO
Warwick has worked in defence, health and taxation and has been involved in analytics for over 20 years. He is a practicing analytics professional and is currently convenor of the Whole of Government Data Analytics Centre of Excellence and is a senior data scientist in Data Science and Special Acquisition Group of the Smarter Data Program of the ATO. He has a BSc from the University of New South Wales and a PhD from the University of New England Australia. His professional interests include digital transformation and innovation, organizational learning, organizational decision making and analytics. He is a former board member of the Institute of Analytics Professionals Australia and is currently a member of the Board of the College of Organizational Psychology of the Australian Psychological Society.
|
 |
| 12:10-12:35 |
Invited Talk: DataSpark’s work
Dr Paul Rybicki
Chief Country Officer
DataSpark, Australia
Chief Country Officer of DataSpark Australia for the SingTel Group, 5 years experience running OTT, TV & Content businesses, 5 years running Insights and Data Analytics functions at News Corp Pay TV operators and 10 years management consulting in telco and media across APAC and Europe
|
 |
| 12:35-13:30 |
Lunch Break |
|
| 13:30-13:55 |
How Can Big Data Analytics Make Impacts?
Data analytics backed by machine learning discover patterns from discriminated data, and build predictive capability from the derived patterns. To date, such data analytics are widely used in the financial market, marketing, insurance, and there is growing demand in infrastructure and transport. The impact of machine learning is in utilising data to gain unique business insights, as well as in providing innovative solutions to better business efficiency. This talk shares general trends in machine learning as well as information gained from collaborative projects with industry partners, particularly in water pipe failure prediction and advanced data analytics in transport. For the smart water pipe failure prediction project, we have worked with more than 30 utilities from around the world to develop a data-driven predictive analytics approach that more accurately predicts pipe failure, and thus offers networks the ability to better target repair and renewal programs. Deploying the technique allows utilities to prioritise capital spending to high risk assets, reduce operational costs of unexpected failure, and minimise the disruption to water supplies and the community. The advanced data analytics in transport is focused on active data fusion for traffic information service, and large scale traffic simulation to provide a highly accurate, real-time traffic information services from an advanced computing platform.
Dr Fang Chen
Group Leader, Enterprise Analytics
DATA61 | CSIRO
Dr. Fang Chen has an outstanding track record in innovation. In the past 20 years, she has created many world-class solutions while working at organisations like the Beijing Jiaotong University, Intel, Motorola and CSIRO. She leads many taskforces with the goal of utilising data analytics and computational platforms with scales and impacts both national and international. She has helped many industries towards excelling by better solutions to increase productivity, profitability and better customer satisfaction. She has achieved great success in many technical solutions and gained industry recognitions such as the ITS (Intelligent Transport System) Australia National Award 2014 and 2015. She is the “Water Professional of the Year” awarded by Australian Water Association (AWA) NSW on her exceptional leadership and achievements in helping water sector through innovative solutions. Dr Chen has more than 250 refereed publications and has filed more than 30 patents in 8 countries. She is also a conjoint professor with the University of New South Wales and adjunct professor with the University of Sydney, who has supervised more than 20 PhD students to finish.
|
 |
| 13:55-14:20 |
Invited Talk: New Directions in Big Data Analytics for Official Statistics
National statistics agencies like the Australian Bureau of Statistics (ABS) produce a comprehensive set of statistical products, such as key economic indicators, population estimates, and measures of social progress. Traditionally, the ABS has relied on data collected from surveys and the administrative programs of government to meet its statistical compilation needs. With the emergence of the Internet as a unified global platform for digital connectivity, diverse new sources of human- and machine-generated data are now available for use in statistical production. The analytical insights derived from these ‘big data’ sources – including commercial transactions, remote imagery, sensor measurements, geospatial positioning, web content, and online user activity – enable the delivery of innovative, timely and cost-effective information solutions for statistical consumers. This presentation outlines the key concepts, methods and technologies that underpin the next generation of ABS analytical capabilities. Examples are presented from the application of a novel analytical platform created by the ABS – the Graphically Linked Information Discovery Environment (GLIDE) – to three significant use cases.
Dr Frederic R Clarke
Director, Emerging Data & Methods
Australian Bureau of Statistics, Australia
Ric Clarke is the Director of Emerging Data and Methods in the Australian Bureau of Statistics (ABS). He leads a multidisciplinary research and development team in the delivery of innovative approaches for the representation, integration and analysis of complex multisource data.
|
 |
| 14:20-14:45 |
Cost effectiveness of big data computation and storage in the cloud – understanding the trade-offs
The size of data is growing exponentially. How do we cost-effectively manage big data generated in our life? This talk uses Astrophysics as an example to illustrate the trade-offs of computation and storage in the cloud. A huge cost can be saved by managing big data appropriately for the real world.
Prof Yun Yang
Swinburne University of Technology, Australia
Dr Yun Yang is a full professor in School of Software and Electrical Engineering, Swinburne University of Technology, Australia. He earned his PhD in computer science from the University of Queensland in 1992. During 1993 – 1996, he worked at CRC for DSTC (Distributed Systems Technology Centre). He then went to Deakin University as an academic before he joined Swinburne in 1999. His research areas include cloud computing, big data, software engineering and services computing. He is on the editorial board of IEEE Transactions on Cloud Computing. He was a panel member for ERA (Excellence in Research for Australia) 2015.
|
 |
| 14:45-15:10 |
Afternoon Tea |
|
| 15:10-15:35 |
Invited Talk
Dr Amy Shi-nash
Head of Data Science
Commonwealth Bank, Australia |
 |
| 15:35-16:00 |
Invited Talk: Modern Analytics in Insurance for a Complex World
Dr Paul Beinat
Director
Neuronworks, Australia
|
 |
| 16:00-16:20 |
Invited Talk: The Manager’s Guide to Solving the Big Data
Conundrum
David Willingham
Senior Application Engineer
Data Analytics, MathWorks
|
 |
| 16:20-17:40 |
- Panel: Developments and Challenges with the Data Economy
Warwick Graco(Moderator), Stuart Russell, Amy Shi-nash, Dickson Lukose, Fang Chen |
|
| 17:40-18:30 |
Networking sponsored by CBA |
Amy Shi-nash |
