AI/DS Executive Workshop

Date: 22/8/2017
Venue: Melbourne Convention and Exhibition Centre (Meeting room 101, MCEC)
Time: 9:00AM – 5:00PM
AI/Data science Executive Workshop: best practice and real-life enterprise analytics case studies for data and business executives and managers to make informed and smarter decisions.

22 Aug: AI/Data science Executive Workshop (Meeting room 101, MCEC)
Time	Talk	Speaker
08:30-09:00	Registration
09:00-09:10	Introduction	Prof Longbing Cao
09:10-10:10	Machine Learning This engaging presentation outlines the capabilities and potentials of machine learning in the age of big data. Prof Geoff Webb  Monash University, Australia Geoff Webb is a leading data scientist. He is Director of the Monash Centre for Data Science and a Technical Advisor to BigML Inc, who have incorporated his best of class association discovery software, Magnum Opus, as a core component of their advanced Machine Learning service. He developed many of the key mechanisms of support-confidence association discovery in the late 1980s. His OPUS search algorithm remains the state-of-the-art in rule search. He pioneered multiple research areas as diverse as black-box user modelling, interactive data analytics and statistically-sound pattern discovery. He has developed many useful machine learning algorithms that are widely deployed. He was editor in chief of the premier data mining journal, Data Mining and Knowledge Discovery from 2005 to 2014. He has been Program Committee Chair of the two top data mining conferences, ACM SIGKDD and IEEE ICDM, as well as General Chair of ICDM. He received the 2016 Australian Computer Society ICT Researcher of the Year Award, the 2016 Australasian Artificial Intelligence Distinguished Research Contributions Award, a 2014 Australian Research Council Discovery Outstanding Researcher Award, the 2013 IEEE Outstanding Service Award, and is an IEEE Fellow.
10:10-10:30	Morning Tea
10:30-11:30	Modern Analytics in the Insurance Industry The general insurance industry has a long history, and throughout this history methods for pricing risks have been on an evolutionary journey. Fifty years ago the state of the art was the minimum bias methods of Bailey and Simon. In 1972 the Generalized Linear Model (GLM) was developed by Nelder and Wedderburn. This has now become the standard analytical framework in the industry. We will look at how well it performs and how well its restricted model structure performs compared to more modern methods. Dr Paul Beinat  Director Neuronworks, Australia Paul Beinat (BSc Mathematics – University of New South Wales, PhD Computer Science – University of Technology Sydney) founded NeuronWorks in 1997. Paul designed and developed Thalamus (now referred to as Talon), a new machine learning technology specifically for insurance applications. In the insurance claims arena he developed the concept of Precedent and Claims Outcome Advisor (COA). These claims solutions are used by some of the world’s largest insurers, on two continents. Previously he had designed and developed Colossus, regarded as one of the largest knowledge based systems in the world. At one stage this software assessed approximately half of all bodily injury claims in the United States. He has also implemented analytics-based systems at a number of Australian, US and UK insurers via EagleEye Analytics Inc, where hewas the Principal Scientist. He is an Adjunct Professor at the University of Technology Sydney.
11:30-12:30	Competencies and practices for data science-driven enterprise and innovation Applied data science offers unique and advanced opportunities for evidence-based enterprise innovation, management and decision-making. However, to get things right is not a trivial task and journey, requiring specialized competencies and practices at an individual, team, and enterprise level. This is particularly critical for managers and chief officers who manage and govern data science projects. As a constituent of this executive workshop, I will share my experience, observations, and lessons about data science competencies and practices in many areas including government services, capital markets, finance, banking, insurance, and telco, building on my past decades of experiences in conducting, managing, and assisting in many large enterprise data science projects as a chief technology officer and then an academic. I will also summarize and discuss the lifecycle enterprise analytics, methods and processes, case studies, and major techniques and methods we invented, which have been successfully applied to enterprise data science. Prof Longbing Cao  University of Technology Sydney, Australia Longbing Cao is a professor at the University of Technology Sydney. He is the founding director of the Advanced Analytics Institute at the University of Technology Sydney. He holds a PhD in Computing Science and another PhD in Pattern Recognition and Intelligent Systems. He has been working on promoting data science and analytics research, education, development and engagement since he was a CTO and then joined academia. Focusing on real challenging problems-driven research, he proposed/dedicates to several concepts with supporting theories, tools and applications, including behaviour informatics, non-IID learning, and domain driven data mining, in addition to issues generally concerned in data mining and machine learning. In education and professional services, he established the Data Science and Knowledge Discovery lab at UTS in 2007, founded the Advanced Analytics Institute and established the degrees Master of Analytics (Research) and PhD in Analytics at UTS in 2011, founded the IEEE Task Force on Data Science and Advanced Analytics (DSAA) and IEEE Task Force on Behavior, Economic and Soci-cultural Computing In 2013, established the IEEE Conference on Data Science and Advanced Analytics (DSAA) and the ACM SIGKDD Australia and New Zealand Chapter in 2014, and started the International Journal of Data Science and Analytics with Springer in 2015. He led several data events including the Big Data Summit and KDD2015 in Sydney. In development and engagement, his team has successfully delivered many large analytical projects for government and business organizations in Australia and overseas, resulting in significant dollar savings and mentioned in government, industry, media and OECD reports.
12:30-13:30	Lunch
13:30-15:00	Enterprise Analytics: Better Practice Discussions	Longbing Cao, Paul Beinat
15:00-15:30	Afternoon Tea

Who Should Attend:

• Enterprise senior executives (including CDO, CAO, CDS, etc.)
• Government information, data and analytics directors and managers
• Head of data science, head of analytics, etc. for corporate analytics
• Policy-making managers and directors
• Head and managers of innovation and entrepreneurship
• Business development directors
• Senior consulting service directors and managers
• Decision strategists

Highlights:

• Designed for enterprise executives who lead AI/DS strategies and portfolio
• The vision and future of AI/DS and data/intelligence economy
• The big value, trends, and potential of data to economy and smart decision
• The global perspectives, experiences, and best practices of data and intelligence economy
• Informed decisions on enterprise-wide data decisions, solutions, practices and competency
• Real problem-based deep analysis of enterprise transformation gaps, strategies and opportunities
• Interactive and personalized mentoring, teamwork, case studies, arguments, and collaborations
• World top industry and research leaders in AI and DS as mentors and collaborators
• Collaborative work with senior peers from different domains and backgrounds
• Data and intelligence-driven industrialization, enterprise, productivity, and decision
• Competencies and capabilities for enterprise transformation
• Private meeting opportunities with world top leaders

Tentative Program/Activities:

• Values, benefits and impact of undertaking data science and big data analytics
• Advances and trends of artificial intelligence 2.0
• Advances and trends of data science
• Data science for strategic enterprise and government transformation
• Enterprise and government opportunities in data science/AI 2.0
• Methodologies, methods, tools and skillset for undertaking data science/AI strategies and projects
• Best practices for enterprise/corporate data science and big data analytics
• Enterprise gap analysis in data science and intelligence economy
• Building data maturity: data scientist capabilities and enterprise competencies
• Development plan for staff and enterprise maturity of data science
• Risk and trust management in data economy and industrialization
• The good, bad and ugly of data/intelligence economy and transformation
• Real-world enterprise case studies
• Interactive and personalized mentoring, teamwork, case studies, and collaborations
• Teamwork with senior peers from different domains and backgrounds
• Lessons and experiences sharing between data leaders