李青，教授，博士生导师，香港城市大学多媒体工程研究中心主任、电脑科学系教授，主要研究领域包括多媒体数据管理、概念建模、数据挖掘、社交媒体与Web服务计算等。他在相关领域发表了300多篇的国际会议与期刊文章，是NSFC 海外杰青获得者。担任过ACM Transactions on Internet Technology, IEEE Transactions on Knowledge and Data Engineering 的副编，也是WWW journal, Data & Knowledge Engineering, Journal of Web Engineering 等期刊的编委。他现任香港万维网科技学会会长、国际万维网信息系统工程(WISE)学会副会长、CCF数据库专委会常务委员、CCF大数据专家委员会委员等，同时在多个国际会议（包括ACM RecSys, DASFAA, ER, ICWL, IEEE U-Media）出任指导委员会委员。
报告摘要：Eliciting true information from the input is of critical importance for meaningful output. There are two ways to secure true information. One is mechanism design where agents reporting wrong information will not gain them any benefit. The other is data mining where true information is dug out from multi-sourced (and perhaps multi-modal) data upon which cross validation can be carried out.
About mechanism design, we use facility location games as an example to illustrate how the truthfulness can be guaranteed by sacrificing some efficiency. Various models are discussed for this setting of mechanism design without money. Especially for one of the variants, we consider how agents affect each other in their utilities. We assume that the coefficients are already known as input when the mechanism is applied. However, in reality, the way people (agents) affect each other needs to be learned from historical data. Therefore, we also analyze how to learn those coefficients in the underlying social networks. For data mining, we use social event discovery as an example, where multi-sourced social media data is collected, integrated, and fused, upon which clustering and classification algorithms are then applied to discover or detect significant/interested events. In dealing with incomplete or inconsistent data from multiple sources, a multi-dimensional model is devised to facilitate cross checking and validation, thereby achieving the objective of digging out true information.