James A. Evens（美国芝加哥大学社会学教授） 演讲题目：Diversity and Discovery

报告摘要：I begin by examining how scientists and inventors move through ideas over the life-course, at first quickly, then more and more slowly, in combinations that ossify and eventually form boundaries in science. Then I connect this to how breakthrough discoveries and inventions cross these boundaries through unexpected combinations of contents including problems, methods, and natural entities across diverse contexts such as journals, subfields, and conferences. Drawing on data from tens of millions of research papers, patents, and researchers, I construct models that predict next year’s content and context combinations with an AUC of 95% based on embeddings constructed from high-dimensional stochastic block models, where the improbability of new combinations itself predicts up to 50% of the likelihood that they will gain outsized citations and major awards. Most of these breakthroughs occur when problems in one field are unexpectedly solved by researchers from a distant other. These findings demonstrate the critical role of surprise in advance, and enable evaluation of scientific institutions ranging from education and peer review to awards in supporting it. I demonstrate how emerging artificial intelligence approaches for knowledge discovery that explicitly incorporate the distribution of people involved can double performance in predicting future advances over those that consider the distributions of ideas alone. This work suggests a path toward creating the most, but also the least human artificial intelligences, and to optimally design collective cognitive diversity that radically augments human intelligence.
个人简介: James Allen Evans, Professor, Department of Sociology, Committee on Conceptual and Historical Studies of Science, and the College, University of Chicago. His research interests are described as follows: augmented, artificial and collective intelligence; innovation; science of science; complex systems; sociology of knowledge; computational social science; empirical epistemology; science and technology policy; data science; science, technology and society; history and philosophy of science; sociology of health/medicine; organizations; economic sociology; sociology of work.

王海勋 (IEEE Fellow、Instacart副总裁和杰出科学家) 演讲题目：AI and its physical and societal impact

报告摘要：Technologies have already transformed our lives, but its deep physical and scoietal impact has just begun to emerge. In this talk, I will discuss the limitation, the potential, and the danger of technologies as they become an increasingly bigger part of our lives.
个人简介: Haixun Wang is an IEEE fellow and a VP of Engineering and Distinguished Scientist at Instacart. Before joining Instacart, he was a VP of Engineering and Distinguisited Scienitst at WeWork, a Director of Natural Language Processing at Amazon, and he worked on machine learning, knowledge discovery, and data management at Facebook, Google, Microsoft, and IBM. He has published more than 150 research papers in referred international journals and conference proceedings. He is Editor in Chief of the IEEE Data Engineering Bulletin. He has served as PC chairs of academia conferences and assoicate editors of academia journals.

刘欢（ACM Fellow、AAAI Fellow、美国亚利桑那州立大学计算机科学与工程学院教授） 演讲题目：Privacy Paradox on Social Media – Can Users Have both Privacy and Utility?

报告摘要：Social media users often encounter the so-called privacy paradox. The good utility of an online service seems to entail the knowledge of a user’s unique needs via their personal data. It is therefore inevitable to trade user’s privacy for utility. In this talk, we ask if users can still have both privacy and utility in an attempt to circumvent the privacy paradox. Since users may have different views toward privacy, user privacy is a complex issue and needs protection from Law, Policy, and Technology. We present some discussion from a technology viewpoint based on our own research.
个人简介: Huan Liu, ACM Fellow、AAAI Fellow, Professor, Computer Science and Engineer, School of Computing, Informatics, and Decision Systems Engineering. His research focuses on developing computational methods for data mining, machine learning, and social computing, and designing efficient algorithms to enable effective problem solving ranging from basic research, text/Web mining, bioinformatics, image mining, to real-world applications.

傅晓明（欧洲科学院院士、德国哥廷根大学数学与计算机科学学院终身教授） 演讲题目：A Cross-Platform Consumer Behavior Analysis of Large-Scale Mobile Shopping Data

报告摘要：The proliferation of mobile devices especially smart phones brings remarkable opportunities for both industry and academia. In particular, the massive data generated from users’ usage logs provides possibilities for stakeholders to learn about consumer behaviors with the aid of data mining. In this article, we examine consumer behaviors across multiple platforms based on a large-scale mobile Internet dataset from a major telecom operator. The dataset covers over 9.7 million users from two regions, 1.4 million among which visited e- commerce platforms within one week of our study. In this study we attempt to answer several questions such as 1) how the users’ locations (and more generally spatiotemporal factors) influence their shopping behaviors; 2) how much time it takes for a user to decide to buy a product when confronted with many alternative options from multiple platforms; and 3) whether users exhibit signs of loyalty to preferred shopping platforms? Our extensive analysis of the consumer behavior dataset shows that users from an economically more developed region are quicker to make shopping decisions than those from less developed regions. Also, among the multiple e-commerce platforms available, most mobile users are loyal to their favorable platforms. Furthermore, people (more than 60% of users) tend to make quick decisions to buy something online, within less than 30 minutes. These new insights could provide useful information for e-commerce future strategy planning.
个人简介: Prof. Xiaoming Fu received his Ph.D. in computer science from Tsinghua University, Beijing, China in 2000. He was then a research staff at the Technical University Berlin until joining the University of Goettingen, Germany in 2002 as assistant professor, where he has been a full professor of computer science and heading the Computer Networks Group since April 2007. He has spent research visits at universities of Cambridge, Columbia, UCLA, Oregon, Tsinghua, Nanjing, Fudan, Uppsala, Sorbonne and Victoria. Prof. Fu's research interests include architectures, protocols, and applications of networked systems, including Internet and mobile computing, cloud computing, social computing and big data analytics. He is currently an editorial board member of IEEE Transactions on Network Science and Engineering, IEEE Transactions on Network and Service Management, and Nature-Springer Computer Science. He has served as secretary (2008-2010) and vice chair (2010-2012) of the IEEE Communications Society Technical Committee on Computer Communications (TCCC), and chair (2011-2013) of the Internet Technical Committee (ITC) of the IEEE Communications Society and the Internet Society. He was the coordinator of FP7 MobileCloud, GreenICN and CleanSky projects and H2020 ICN2020 project, and is currently a PI of EU H2020 COSAFE project on intelligent transportation. He is a fellow of IET, an IEEE Distinguished Lecturer and a member of Academia Europaea.