题目：Towards Quality-Driven Data Crowdsourcing
主讲人： Xiaowen Gong 教授， Auburn University， USA
Data crowdsourcing has found a broad range of applications (including physical sensing tasks such as spectrum sensing, and human intelligence tasks such as image classification), by leveraging the “wisdom” of a potentially large crowd of users. A key metric of crowdsourcing is data accuracy, which relies on the quality of the participating users’ data. To fully exploit the potential of crowdsourcing, it is important to leverage quality-aware crowdsourcing which allocates tasks to users and aggregate users' data based on the users' data quality. However, it needs to overcome the obstacle that data quality is often unknown to the crowdsourcing requester.
In this talk, I will present our recent research on quality-driven crowdsourcing along two directions. In one direction, we devise truthful mechanisms that incentivize users to truthfully report their quality and data to the requester, and truthfully make effort as desired by the requester. In the other direction, we develop online quality learning algorithms that estimate users’ data quality on the fly while allocating tasks to users based on the estimated data quality. These studies draw on synergistic integration of models and methods from statistical inference, mechanism design, and reinforcement learning. We will also discuss future research directions.
Xiaowen Gong is an Assistant Professor in the Department of Electrical and Computer Engineering at Auburn University. He received his BEng degree in Electronics and Information Engineering from Huazhong University of Science and Technology in 2008, his MSc degree in Communications from the University of Alberta in 2010, and his PhD degree in Electrical Engineering from the Arizona State University (ASU) in 2015. From 2015 to 2016, he was a postdoctoral researcher in the Department of Electrical and Computer Engineering at The Ohio State University. His research interests are in the areas of wireless networking and mobile computing, with focuses on crowdsourcing, mobile social networks, fog networking and computing, and privacy in mobile networks. He received the Runner-up Best Paper Award from IEEE INFOCOM 2014 as a co-author, and Palais Outstanding Doctoral Student Award from the school of ECEE at ASU in 2015.