I study the theoretical foundation and practical algorithms for Artificial Intelligence. To build intelligent machines that can tackle challenging reasoning problems under uncertainty, I have pursued answers via studies of Machine Learning, Natural Language Processing, and Interdisciplinary Data Science. More specifically, I am interested in designing scalable inference and learning algorithms to analyze massive datasets with complex structures. In particular, I advance methods in the following research areas: Statistical Relational Learning, Knowledge Representation and Reasoning, Natural Language Processing, Speech, and Computational Social Science. The central focus of my PhD dissertation research is to bring together all areas above and design scalable algorithms for large scale inference problems on knowledge graphs. Meanwhile, I enjoy collaborating with scientists and domain experts of different backgrounds for interdisciplinary research in data science. Currently, I am interested in advancing challenging problems in Artificial Intelligence, such as Natural Language Understanding, Information Extraction, and Learning to Reason.