Social Network Analytics

We focus on a unique phenomenon in social networks – the diffusion of contents, information, and adoption behaviors from one social entity to another. In particular, we investigate three critical and related problems concerning this phenomenon: adoption, persuasion, and link recommendation. That is, the diffusion of contents, information, and adoption behavior is initiated by persuaders and reached to adopters through the linkage structure of a social network. Accordingly, we study the following problems: how to predict adoption probabilities in a social network? how to predict top persuaders in a social network? and how to recommend links for a social network?

Financial Technology

We are interested in designing state-of-the-art analytical tools to solve fundamental problems of Financial Technology (FinTech). We actively explore new opportunities enabled by recent development in artificial intelligence and social network analytics to provide better solutions than traditional methods. One project aims at developing a better industry classification system by designing a novel deep learning algorithm to analyze firms’ annual 10K reports. Another project develops algorithms that can learn hidden relationships among training instances (e.g., hidden relationships among loan applicants) and leverages these learned relationships for better predictions (e.g., predicting the probability of default).

Data Science

We investigate fundamental data science problems. For real world applications, new data are continuously generated. Newly generated data could bring in new knowledge and invalidate part or even all of the earlier discovered knowledge. As a result, a fundamental problem in data science is how to maintain the currency of knowledge discovered from rapidly evolving data sources, namely the knowledge refreshing problem. Data incompleteness is another common problem in data science. For example, product adoption decision in a social network context depends on social influence, entity similarity, structural equivalence, and hidden factors. While it is easy to collect data for the first three factors, hidden factors are unobserved, i.e., observed data are incomplete. Therefore, it is necessary to study how to discover knowledge from incomplete data.