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Our group has conducted a broad range of research over the years, and continues to pursue several areas in depth.

Data mining: Boosted by the industrial needs and supported by numerous developments in machine learning, statistics, artificial intelligence, and database systems, data mining research has now rapidly spanned various fields from computational science, business intelligence, life science, etc. Our interests include developing effective and efficient data analysis techniques for complex data and the related applications, such as mining web, stream data, graph, and other types of complex data from complex data environments.

Social Network: Social Network Analysis (SNA) helps us to better understand how and why we interact with each other, as well as how technology can alter this interaction. Our work is about tracking and modeling information flow in E-mail and blog networks, modeling search processes on real-world social networks, studying the role of social networks in knowledge creation and sharing and building expertise-finding systems.

Uncertain Data: Increasingly, today we need to manage data that is uncertain. The uncertainty can be in the data itself, in the schema, in the mapping between different data instances, or in the user query. We find increasingly large amounts of uncertain data in a variety of domains: in data integration, in scientific data, in information extracted automatically from text, in data from the physical world. The major themes in our study include: (1) query answering from probabilistic views; (2) skyline query on uncertain data; (3) data mining on uncertain data.

Recommendation: All of us have the known the feeling of being overwhelmed by the number of news, advertisements and all kinds of products for sale in E-commerce sites. Recommendation system can dramatically reduce the useless information and help us sift through all the available information to find which is most valuable to us. Our interests focus on finding efficient and intelligent techniques to mine users profiles, usage and click-stream data, the site content, the site structure and domain knowledge for actionable knowledge, and to effectively use the discovered knowledge to enhance the users' Web experience. Moreover, we also explore the vulnerabilities of recommendation and personalization systems in the face of malicious attacks, explore techniques for enhancing their robustness, and examine methods by which attacks can be recognized and possibly defeated.