Keynote Speakers

Algorithmic Crowdsourcing and Applications in Big Data

Prof. Jie Wu
Department of Computer and Information Sciences
Temple University, USA

This talk gives a survey of crowdsourcing applications, with a focus on algorithmic solutions. The recent search for Malaysia flight 370 is used first as a motivational example. Fundamental issues in crowdsourcing, in particular, incentive mechanisms for paid crowdsourcing, and algorithms and theory for crowdsourced problem-solving, are then reviewed. Several applications of algorithmic crowdsourcing applications are discussed in detail, with a focus on big data. The talk also discusses several on-going projects on crowdsourcing at Temple University.

Jie Wu is the Associate Vice Provost for International Affairs at Temple University. He also serves as the Director of Center for Networked Computing and Laura H. Carnell professor in the Department of Computer and Information Sciences. Prior to joining Tempe University, he was a program director at the National Science Foundation and was a distinguished professor at Florida Atlantic University. His current research interests include mobile computing and wireless networks, routing protocols, cloud and green computing, network trust and security, and social network applications. Dr. Wu regularly publishes in scholarly journals, conference proceedings, and books. He serves on several editorial boards, including IEEE Transactions on Service Computing and the Journal of Parallel and Distributed Computing. Dr. Wu was general co-chair/chair for IEEE MASS 2006, IEEE IPDPS 2008, IEEE ICDCS 2013, and ACM MobiHoc 2014, as well as program co-chair for IEEE INFOCOM 2011 and CCF CNCC 2013. He was an IEEE Computer Society Distinguished Visitor, ACM Distinguished Speaker, and chair for the IEEE Technical Committee on Distributed Processing (TCDP). Dr. Wu is a CCF Distinguished Speaker and a Fellow of the IEEE. He is the recipient of the 2011 China Computer Federation (CCF) Overseas Outstanding Achievement Award.

Big Data - Big Application

Prof. Jinjun Chen
Deputy Director - Swinburne Data Science Research Institute
School of Software and Electrical Engineering
Swinburne University of Technology

Right now, Big Data, Data Science or Data Analytics are being on wide interest in industry and academia. During this talk, we will discuss two questions based on my research industry engagement practice.
The first one is business gain from such buzz words. This is a practical question from business. Based on my research, big data means big niche market opportunity for retail industry which can grow up to enhance major markets. For example, by analysing and generalising potential weak connection between previously sparse data sources such as flight booking data and supermarket user data, we can better expand or enhance the market for personal recommendation on flight booking.
The second is how researchers make a full potential to business. A say is "It's not who has the best algorithm that wins. It's who has the most data" by Andrew Ng (Coursea founder). Data is becoming an important resource equally important to oil. While various public datasets are available to academics or researchers for research evaluation, those datasets may not be suitable or useful and timely for researchers. One way to make full potential of big data is to intensively work with industry because they have timely data. More or less, every industry is doing data analysis yet just on their specific purposes. We will brief our research and collaboration with specific industries.

Short Bio:

0Dr Jinjun Chen is a Professor from Swinburne University of Technology, Australia. He is the Deputy Director of Swinburne Data Science Research Institute, and the Director of Swinburne Big Data Lab. He holds a PhD in Information Technology from Swinburne University of Technology, Australia. His research interests include scalability, big data, data science, cloud computing, data intensive systems, data privacy and security, and related various research topics. His research results have been published in more than 130 papers in international journals and conferences, including various IEEE/ACM Transactions.
He received UTS Vice-Chancellor's Awards for Research Excellence Highly Commended (2014), UTS Vice-Chancellor's Awards for Research Excellence Finalist (2013), Swinburne Vice-Chancellor¡¯s Research Award (ECR) (2008), IEEE Computer Society Outstanding Leadership Award (2008-2009) and (2010-2011), IEEE Computer Society Service Award (2007), Swinburne Faculty of ICT Research Thesis Excellence Award (2007). He is an Associate Editor for ACM Computing Surveys, IEEE Transactions on Big Data, IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Cloud Computing, as well as other journals such as Journal of Computer and System Sciences, JNCA. He is the Chair of IEEE Computer Society¡¯s Technical Committee on Scalable Computing (TCSC).


Future of Human-AI Relationship: Cross-Disciplinary Perspectives

Moderator: Qun Jin, Waseda University, Japan
Panelists: Jinjun Chen, Swinburne University of Technology, Australia
Yun Cheng, Hunan University of Humanities, Science and Technology, China
Shaozi Li, Xiamen University, China
Huijuan Lu, China Jiliang University, China
Jie Wu, Temple University, USA

The past decade has witnessed the dramatic development of emerging cyber technologies, such as Ubiquitous Clouds, Big Data, Internet of Things (IoT), Smart City, Machine Learning, Personal Analytics, and etc. Cyber-enabled and data-driven intelligent technology enables Artificial Intelligence (AI) to an unprecedented level. It may bring many possibilities and opportunities for humans in pursuit of Quality of Life (QoL) and well-being. On the other hand, it causes big concern about the relationship between human and AI. In this panel, the moderator and panelists will present and discuss their views on future of human-AI relationship from the cross-disciplinary perspectives of their different fields.

Short Bio of the Moderator:
0Qun Jin is a tenured full professor at the Department of Human Informatics and Cognitive Sciences, Faculty of Human Sciences, Waseda University, Japan. He has been engaged extensively in research works in the fields of computer science, information systems, and social and human informatics. He seeks to exploit the rich interdependence between theory and practice in his work with interdisciplinary and integrated approaches. Dr. Jin has published more than 200 refereed papers in the academic journals, such as ACM Transactions on Intelligent Systems and Technology, IEEE Transactions on Human-Machine Systems, IEEE Transactions on Learning Technologies, and Information Sciences (Elsevier), and international conference proceedings in the related research areas. He has served as a general chair, program chair and TPC member for numerous international conferences, and editor-in-chief, associate editor, editorial board member and guest editor for a number of scientific journals. His recent research interests cover human-centric ubiquitous computing, behavior and cognitive informatics, big data, personal analytics and individual modeling, cyber security, AI and intelligent technology, cyber-enabled applications in healthcare, and computing for well-being. He is a member of IEEE, IEEE CS, and ACM, USA, IEICE, IPSJ, and JSAI, Japan, and CCF, China.

1 International Conference on Information Science and Control Engineering