Since July 2018, I have been an Assistant Professor at the School of Computing and Information Systems, Singapore Management University (SMU). Prior to joining SMU, I was a data scientist at DBS Bank, and a research scientist at A*STAR. I obtained a PhD Degree in Computer Science from the University of Illinois at Urbana-Champaign in 2014 on a fully funded scholarship from A*STAR, and Bachelor of Computing with First Class Honors from National University of Singapore in 2009 as the top student in Computer Science.
Research Interests and Projects
My general interest lies in the broad areas of data mining, machine learning and artificial intelligence. More specifically, I am working on the following research topics: (1) Learning on graphs and information networks; (2) Web and social media analytics; (3) Recommendation systems; (4) Application of knowledge graphs on data mining problems. Some of my research projects are summarized below, and you can read my research statement and the full list of publications.
Meta-learning on graphs
Meta-learning, also known as learning-to-learn, has shown great promise in many domains such as computer vision and natural language processing. It is particularly useful to the scenario where there is a need to adapt to new input or task (e.g., few-shot classification on novel classes), such that the adaptation process can be learned instead of hand designed. Specific techniques vary, such as the optimization-based MAML and metric-based ProtoNet, and in a broader sense hypernetworks. In this project, we investigate the emerging trend of meta-learning on graphs, which have found important use cases for graph-based learning and mining, such as few-shot or semi-supervised node classification [AAAI21a, SIGIR21], tail node embedding [CIKM20, KDD21], graph neural networks [AAAI21b, IJCAI21], and cold-start recommendation on graphs [KDD20].
Metagraph: Semantics-guided graph representations
Heterogeneous Information Networks (HIN) often model complex, multi-typed relationships between different types of objects. Metagraphs [ICDE16, TKDE19], which model recurring subgraph patterns on HINs, emerge as a powerful tool to capture the rich semantics on a HIN. Thus, metagraphs can be used to derive powerful graph representations [TKDE20] or pre-train graph neural network models [CIKM21] on HINs. The learned representations enable a wide variety of downstream applications, such as disease gene prediction [Method17].