Keynote Speeches and Industrial Invited Talks
Keynote Speech I: Graph Processing: Applications, Challenges, and Advances.
Abstract:
Graph data are key parts of Big Data and widely used for modelling complex structured data with a broad spectrum of applications. Over the last decade, tremendous research efforts have been devoted to many fundamental problems in managing and analyzing graph data. In this talk, I will cover various applications, challenges, and recent advances. We will also look to the future of the area.
Graph data are key parts of Big Data and widely used for modelling complex structured data with a broad spectrum of applications. Over the last decade, tremendous research efforts have been devoted to many fundamental problems in managing and analyzing graph data. In this talk, I will cover various applications, challenges, and recent advances. We will also look to the future of the area.
Prof. Xuemin Lin
Scientia Professor, University of New South Wales
Xuemin Lin is a UNSW Scientia Professor in the School of Computer Science and Engineering at the University of New South Wales. Currently, he is the head of database research group in the School of Computer Science and Engineering at UNSW. Xuemin is elvated to Fellow of IEEE in Nov, 2015. Xuemin got his PhD in Computer Science from the University of Queensland (Australia) in 1992 and his BSc in Applied Math from Fudan University (China) in 1984. During 1984-1988, he studied for PhD in Applied Math at Fudan University.
Keynote Speech II: Big Data, AI, and HI, What is the next?
Abstract:
Recently, AI has become quite popular and attractive, not only to the academia but also to the industry. The successful stories of AI on Alpha-go and Texas hold 'em games raise significant public interests on AI. Meanwhile, human intelligence is turning out to be more sophisticated, and Big Data technology is everywhere to improve our life quality. The question we all want to ask is “what is the next?". In this talk, I will discuss about DHA, a new computing paradigm, which combines big Data, Human intelligence, and AI. First I will briefly explain the motivation of DHA. Then I will present some challenges and possible solutions to build this new paradigm.
Recently, AI has become quite popular and attractive, not only to the academia but also to the industry. The successful stories of AI on Alpha-go and Texas hold 'em games raise significant public interests on AI. Meanwhile, human intelligence is turning out to be more sophisticated, and Big Data technology is everywhere to improve our life quality. The question we all want to ask is “what is the next?". In this talk, I will discuss about DHA, a new computing paradigm, which combines big Data, Human intelligence, and AI. First I will briefly explain the motivation of DHA. Then I will present some challenges and possible solutions to build this new paradigm.
Prof. Lei Chen
Professor, Hong Kong University of Science and Technology
Lei Chen received his BS degree in Computer Science at Tian Jin University ,P.R.China(BS 94), and an MA degree in computer science at Asian Institute of Technology (AIT) Asian Institute of Technology (MS 97). He received a Ph.D. degree in Computer Science at University of Waterloo. His research interests have been focused on Data-driven machine learning, Crowdsourcing-based data processing, Uncertain and probabilistic databases, Web information management, Multimedia systems.
Keynote Speech III: Differential Privacy in the Local Setting.
Prof. Ninghui Li
Abstract:
Differential privacy has been increasingly accepted as the de facto standard for data privacy in the research community. Recently, techniques for satisfying differential privacy (DP) in the local setting, which we call LDP, have been deployed. Such techniques enable the gathering of statistics while preserving privacy of every user, without relying on trust in a single data curator. Companies such as Google, Apple, and Microsoft have deployed techniques for collecting user data while satisfying LDP. In this talk, we will discuss the state of the art of LDP. We survey recent developments for LDP, and discuss protocols for estimating frequencies of different values under LDP, and for computing marginal when each user has multiple attributes. Finally, we discuss limitations and open problems of LDP.
Differential privacy has been increasingly accepted as the de facto standard for data privacy in the research community. Recently, techniques for satisfying differential privacy (DP) in the local setting, which we call LDP, have been deployed. Such techniques enable the gathering of statistics while preserving privacy of every user, without relying on trust in a single data curator. Companies such as Google, Apple, and Microsoft have deployed techniques for collecting user data while satisfying LDP. In this talk, we will discuss the state of the art of LDP. We survey recent developments for LDP, and discuss protocols for estimating frequencies of different values under LDP, and for computing marginal when each user has multiple attributes. Finally, we discuss limitations and open problems of LDP.
Prof. Ninghui Li
Professor, Purdue University
Ninghui Li is a Professor of Computer Science at Purdue University. He has been doing research in security and privacy, including data privacy, applied cryptography, access control, trust management, and human factors in security and privacy. He has published over 150 referred papers in these areas. His 2007 paper ``t-Closeness: Privacy Beyond k-Anonymity and l-Diversity'' recently received the ICDE 2017 Influential Paper award.
Industrial Invited Talk I: Huawei Blockchain Service.
Abstract:
Blockchain is potential to transform business operation models and to create new foundation for global economic and social systems with disruptive innovation. Huawei aims to drive enterprise blockchain applications and has already launched Huawei Blockchain Service(BCS) on Huawei public cloud. In this talk, we will first present our BCS architecture and experiences on building BCS. Then, technical innovations to address major blockchain challenges, including consensus performance, security and privacy protection, usability of blockchain, will be presented with illustrative use cases. Finally, we will share our experiences/lesson learns of promoting blockchain in enterprise applications and our vision of blockchain technology.
Blockchain is potential to transform business operation models and to create new foundation for global economic and social systems with disruptive innovation. Huawei aims to drive enterprise blockchain applications and has already launched Huawei Blockchain Service(BCS) on Huawei public cloud. In this talk, we will first present our BCS architecture and experiences on building BCS. Then, technical innovations to address major blockchain challenges, including consensus performance, security and privacy protection, usability of blockchain, will be presented with illustrative use cases. Finally, we will share our experiences/lesson learns of promoting blockchain in enterprise applications and our vision of blockchain technology.
Dr. Zhao Cao
Huawei Blockchain, caozhao1@huawei.com
Dr. Zhao Cao is a technical expert and architect of Blockchain at Huawei where he leads the blockchain team and released Huawei Blockchain Service. Before joining Huawei, he was associate professor at Beijing institute of Technology, research staff member and senior researcher at IBM Research and HP Labs. He received his Ph.D and B.E. degree in computer science from Beijing Institute of Technology in 2010 and 2000, respectively. He was a visiting student supervised by Prof. Yanlei Diao at the University of Massachusetts, Amherst from September 2008 to March 2010. His research interests include distributed system, blockchain, data management, big data, stream data processing, etc. He was PC member of VLDB 2017, ICDE2017/2018, ICDCS 2016. He is a Standing Committee member of the CCF Blockchain Technical Committee and a member of the CCF database Technical Committee.
Industrial Invited Talk II: From Big Data to Intelligent Applications with Case Studies in Healthcare Domain.
Abstract:
Besides theoretical research, the growth of AI industry will highly rely on the development of applications. While the real valuable applications will based on the development of AI technologies. The development of AI technologies will highly rely on machine computable big data. In this talk, Dr. Jun Yan will firstly use some real cases in healthcare domain of Chinese market to show the technical challenges of big data, say, how should we transfer from “data big” to computable “big data”. And then, he will introduce the related AI technologies with a big roadmap to show how real world applications are solved using AI technologies based on big data. Finally, he will show some intelligent application showcases in different research areas of healthcare domain and give his ongoing work to help the growth of AI industry.
Besides theoretical research, the growth of AI industry will highly rely on the development of applications. While the real valuable applications will based on the development of AI technologies. The development of AI technologies will highly rely on machine computable big data. In this talk, Dr. Jun Yan will firstly use some real cases in healthcare domain of Chinese market to show the technical challenges of big data, say, how should we transfer from “data big” to computable “big data”. And then, he will introduce the related AI technologies with a big roadmap to show how real world applications are solved using AI technologies based on big data. Finally, he will show some intelligent application showcases in different research areas of healthcare domain and give his ongoing work to help the growth of AI industry.
Dr. Jun Yan
Chief AI Scientist at YiDu Cloud, jun.yan@yiducloud.cn
Dr. Jun Yan received the Ph.D. degree in digital signal processing and pattern recognition from the department of information science, school of mathematical science, Peking University, P.R. China. During his Ph.D., he has been a research intern of MSRA from 2003 to 2005 and awarded as Microsoft fellow in 2004. Before join Microsoft, he has been a research associate at Harvard Medical School, Cambridge, MA, in 2005. He joined Microsoft Research Asia (MSRA) from 2006 and finally became the research manager of the Data Mining and Enterprise Intelligence group in MSRA. His research interests are on large scale knowledge extraction and mining, machine learning, information retrieval and artificial intelligence for enterprise in vertical domains etc. So far, he has successfully incubated tens of technologies, which have been used in Microsoft products. In academia, he has more than 80 quality papers published in referred conferences and journals, including SIGKDD, SIGIR, WWW, ICDM, AAAI, CIKM, TKDE, etc. He has been the PC members of international conferences SIGKDD, SIGIR etc. and is also reviewers of journals articles TKDE, TPAMI etc. He joined YiduCloud as chief AI scientist in 2017.
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