Prof. Yew-soon Ong
Talk Title:
Generalizable Optimization Intelligence for the Cloud and Edge
Abstract:
Traditional Optimization tends to start the search from scratch by assuming zero prior knowledge about the task at hand. Generally speaking, the capabilities of optimization solvers do not automatically grow with experience. In contrast however, humans routinely make use of a pool of knowledge drawn from past experiences whenever faced with a new task. This is often an effective approach in practice as real-world problems seldom exist in isolation. Similarly, practically useful artificial systems are expected to face a large number of problems in their lifetime, many of which will either be repetitive or share domain-specific similarities. This view naturally motivates advanced optimizers that can replicate human cognitive capabilities, leveraging on lessons learned from the past to accelerate the search towards optimal solutions of never before seen tasks. With the above in mind, this talk aims to shed light on recent research advances in the field of global black-box optimization that champion the general theme of ‘Generalizable Optimization Intelligence for the Cloud and Edge’. A brief overview of associated algorithmic developments in memetic computation and Bayesian optimization shall be considered, with illustrative examples of adaptive knowledge transfer across problems from diverse areas, including, operations research, engineering design, and neuro-evolution.
Bio:
Yew-soon Ong received a PhD degree for his work on Artificial Intelligence in complex design from the University of Southampton, United Kingdom in 2003. He is currently a President’s Chair Professor of Computer Science, Professor (Cross Appointment) with School of Physical and Mathematical Science at the Nanyang Technological University, Singapore, and holds the position of Chief Artificial Intelligence Scientist at the Agency for Science, Technology and Research of Singapore. Concurrently he is Director of the Data Science and Artificial Intelligence Research Center, co-Director of the Singtel-NTU Cognitive & Artificial Intelligence Joint Lab and co-Director of the A*Star SIMTECH-NTU Joint Lab on Complex Systems. He was Chair of the School of Computer Science and Engineering, Nanyang Technological University from 2016-2018, Lead of the Data Analytics & Complex System Programme in the Rolls-Royce@NTU Corporate Lab from 2013-2016 and Director of the Centre for Computational Intelligence from 2008-2015. His research interest lies in artificial & computational Intelligence, mainly optimization intelligence and machine learning. He is a Fellow of the IEEE and Editor-In-Chief of the IEEE Transactions on Emerging Topics in Computational Intelligence. He was listed among the World’s Most Influential Scientific Minds and a Thomson Reuters Highly Cited Researcher. Several of his research publications have received IEEE outstanding paper awards.
Prof. Longbing Cao
Talk Title:
Some of Critical Challenges and Opportunities in Data Science
Abstract:
While the concept ‘data science’ has been introduced for half a century, what fundamentally challenges today’s early stage of data science research and applications? This talk attempts to explore several of them: how to learn from real-life data, behaviors and problems that are (1) not independent and identically distributed (non-IID), (2) massive yet extremely sparse, (3) ultrahigh-dimensional, (4) dynamic, and (5) of low quality? Recent work will be illustrated to explore the above challenges in terms of non-IID learning, large-scale statistical learning, and light-weighted deep representation and learning for outlier detection, recommender systems, and enterprise data science.
Bio:
Longbing Cao is a professor at the University of Technology Sydney (UTS) and an ARC Future Fellow (Level 3). He holds a PhD in Computing Science from UTS and another PhD in Pattern Recognition and Intelligent Systems from Chinese Academy of Sciences. In addition to over 300 publications and four monographs, his recent book on Data Science Thinking was published in 2018 by Springer. Since 2005, he started to promote data science research, education, development and enterprise applications and bridge the gaps between cutting-edge research on original real-life problems and impactful business transformation, where he has dedicated to areas including actionable knowledge discovery, agent mining, behavior informatics, complex intelligent systems, and non-IID learning, in addition to more general issues in artificial intelligence, knowledge discovery, machine learning, and recommender systems. In data science and analytics, he established the Data Science and Knowledge Discovery lab at UTS in 2007, the university’s research institute Advanced Analytics Institute and the degrees Master of Analytics (Research) and PhD in Analytics at UTS in 2011, the IEEE Task Force on Data Science and Advanced Analytics (DSAA) in 2013, the IEEE Conference on Data Science and Advanced Analytics (DSAA) and the ACM SIGKDD Australia and New Zealand Chapter in 2014, and the International Journal of Data Science and Analytics with Springer in 2015. He served as program and general chairs of conferences such as KDD. His enterprise data science innovation work has contributed to government and business in over 10 domains, resulting in billions of dollars in savings for industry and government and special mentions in government, industry, media and OECD reports. His leadership in data science was recognized by the 2019 Eureka Prizes for Excellence in Data Science. More about his Data Science Lab at www.Datasciences.org.
Prof. Michael Sheng
Talk Title:
Searching the Internet of Things: The Next Grand Challenge
Abstract:
The Internet of Things (IoT) is widely regarded as an important technology to change the world in the coming decade. Indeed, IoT will play a critical role to improve productivity, operational effectiveness, decision making, and to identify new business service models for social and economic opportunities. While IoT-based digital strategies and innovations provide industries across the spectrum with exciting capabilities to create a competitive edge and build more value into their services, there are still significant technical gaps in making IoT services a reality, specially on effectively managing large volume of IoT devices and information generated from them. In this talk, I will briefly introduce the background of IoT, overview my research team’s more than 10-year research and implementation activities on IoT, and also discuss some future research directions.
Bio:
Dr. Michael Sheng is a full Professor and Head of Department of Computing at Macquarie University. Before moving to Macquarie, Michael spent 10 years at School of Computer Science, the University of Adelaide (UoA). Michael holds a PhD degree in computer science from the University of New South Wales (UNSW) and did his post-doc as a research scientist at CSIRO ICT Centre. From 1999 to 2001, Sheng also worked at UNSW as a visiting research fellow. Prior to that, he spent 6 years as a senior software engineer in industries.
Prof. Sheng’s research interests include Internet of Things (IoT), data analytics, Web technologies, and service computing. He has more than 370 publications as edited books and proceedings, refereed book chapters, and refereed technical papers in journals and conferences including ACM Computing Surveys, ACM TOIT, ACM TOMM, ACM TKDD, VLDB Journal, Computer (Oxford), IEEE TPDS, TMC, TKDE, DAPD, IEEE TSC, WWWJ, IEEE Computer, IEEE Internet Computing, Communications of the ACM, VLDB, ICDE, ICDM, IJCAI, CIKM, EDBT, WWW, ICSE, ICSOC, ICWS, and CAiSE. His research has been highly cited by his international peers (9,760+ citations, 15 research papers received 100+ citations. The highest cited single paper received 1,530+ citations). Prof. Sheng has been invited to give keynotes at a number of international conferences and served as Conference General Chair or Program Chair for several top international conferences in his areas.
Prof. Michael Sheng is the recipient of AMiner Most Influential Scholar in IoT Award (2019), ARC Future Fellowship (2014), Chris Wallace Award for Outstanding Research Contribution (2012), and Microsoft Research Fellowship (2003). For more information, please check his homepage: http://web.science.mq.edu.au/~qsheng/
… more to be added.