My research spans machine learning theories and algorithms, with special interests in transfer learning, deep learning, and predictive learning. In particular, my research is persistently dedicated to advancing all areas of transfer learning, including domain adaptation, inductive transfer learning, unsupervised transfer learning, multi-task learning, few-shot learning, and learning to learn.

    I am leading the Machine Learning Group. My team is interested in developing foundational theories, effective algorithms, and efficient systems of machine learning to make sense of out-of-distribution (OOD) data in inference, reasoning, and decision making. We focus on real applications of machine intelligence and data science, including text, image & video understanding and scientific data analysis.