School of Computing, Informatics, and Decision Systems Engineering
Arizona State University
Tempe, Arizona, USA
Guoliang Xue is a Professor of Computer Science and Engineering at Arizona State University. He earned a PhD degree in Computer Science in 1991 from the University of Minnesota, an MS degree in Operations Research in 1984, and a BS degree in Mathematics in 1981, both from Qufu Normal University. His research interests include resource allocation in computer networks, security and survivability issues in networks, and big data enabled machine learning. He is a recipient of Best Paper Award at IEEE ICC’2012 and IEEE MASS’2011, as well as a Best Paper Runner-up at IEEE ICNP’2010. He is an Area Editor of IEEE Transactions on Wireless Communications for the Wireless Networking Area overseeing 12 editors. He is a past editor of IEEE/ACM Transactions on Networking, and Computer Networks. He was a TPC co-chair of IEEE INFOCOM’2010 and a co-General Chair of IEEE CNS’2014. He was a Keynote Speaker at IEEE LCN’2011, IEEE ICNC’2014, and IFIP WWIC’2018. He is an IEEE Fellow and served as the VP-Conferences of the IEEE Communications Society from January 2016 to December 2017.
Title: Opportunities and Challenges in Using the Power of the Crowd: Incentive Mechanisms, Truth Discovery, and Robustness
With the proliferation of smart mobile devices and the popularity of social networks, crowdsourcing has emerged as a new computing and sensing paradigm, which uses collective power/intelligence of the crowd to accomplish computing or sensing tasks. For crowdsourcing to be useful, we need good incentive mechanisms to attract more users to participate in the activity, we need reliable methods to find the truth from the crowdsourced data. The system also needs to be robust against various attacks, and be able to resolve dispute between the task owners and the work providers. In this talk, we will discuss the crowdsourcing computing paradigms, truthful incentive mechanisms, Sybil attacks and counter-measures, truth discovery via supervised learning and unsupervised learning, and dispute resolution, along with open research issues.
Department of Computing
The Hong Kong Polytechnic University
Song Guo is a Full Professor at Department of Computing, The Hong Kong Polytechnic University. His research interests are mainly in the areas of big data, cloud computing and networking, and distributed systems. His work was recognized by the 2016 Annual Best of Computing in ACM Computing Reviews. He is the recipient of the 2017 IEEE Systems Journal Annual Best Paper Award and other five Best Paper Awards from IEEE/ACM conferences. Prof. Guo was an Associate Editor of IEEE TPDS and an IEEE ComSoc Distinguished Lecturer. He is now on the editorial board of IEEE TCC, IEEE TETC, IEEE TSUSC, IEEE TGCN, IEEE Network, etc. Prof. Guo also served as General and TPC Chair for numerous IEEE conferences. He currently serves as a Director and Member of the Board of Governors of ComSoc.
Title: Distributed Learning for Big Data Analytics: From Cloud to Edge
When accessing cloud-hosted modern applications, users often suffer a significant latency due to the long geo-distance to the central cloud. Edge computing thus emerges as an alternative paradigm that can reduce this latency by deploying services close to users. In this talk, we will analyze the methodology and limitations of popular approaches for supporting AI services on geo-distributed systems along the evolution from cloud computing to edge computing. In particular, we shall discuss how to deal with different sets of challenges in training and inference, the two phases of machine learning based applications, over heterogeneous geo-distributed systems. We shall also present our recent studies on data driven resource management among networked collaborative edges.