SIAM Conference on Data Mining, Cincinnati, OH, May 2020, to appear.Ĭhen Ye, Hongzhi Wang, Kangjie Zheng, Jing Gao, Jianzhong Li.
Rare Disease Prediction by Generating Quality-Assured Electronic Health Records. International Conference on Distributed Computing Systems, Singapore, June 2020, to appear.įenglong Ma, Yaqing Wang, Jing Gao, Houping Xiao, Jing Zhou. Towards Differentially Private Truth Discovery for Crowd Sensing Systems. Yaliang Li, Houping Xiao, Zhan Qin, Chenglin Miao, Lu Su, Jing Gao, Kui Ren, Bolin Ding. In addition to the research advances, this project contributes to educational innovation, as the proposed methods are applied to educational methodologies such as peer assessment and question answering. From the practical perspective, the proposed methods are adapted to tackle challenging problems in various applications such as transportation, healthcare and education to enable new insights into these domains. From the theoretical perspective, fundamental questions regarding the confidence in the estimated reliability and the convergence of the proposed methods are explored. These investigations are integrated with the exploration of both theoretical and practical aspects of the proposed methods. 2) Effective privacy protection and budget allocation mechanisms are designed to better motivate active crowdsourcing. Other valuable information sources, such as spatial-temporal, user influence, and textual data, are leveraged to effectively detect reliable information from these observations. In particular, this project develops novel methods to mine reliable information by taking into consideration various properties of crowdsourcing: 1) Crowdsourcing platforms collect users' observations about certain objects. Through integrating data from various sources, this project addresses information veracity, which will benefit the many applications where crowdsourced data are ubiquitous but veracity can be suspect. This project identifies important research questions in the task of mining reliable information from noisy and unreliable crowdsourced data, and pursues an integrated research and education plan to address these questions. The main obstacle in building such a system lies in the problem of information veracity, i.e., individual users might provide unreliable or even misleading information. Such a system could vastly improve the efficiency and cost of transportation, healthcare, and many other applications. The confluence of these enormous crowdsourced data can contribute to an inexpensive, sustainable and large-scale decision system that has never been possible before. With the proliferation of mobile devices and social media platforms, any person can publicize observations about any activity, event or object anywhere and at any time.
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