1. Privacy Preserving Machine Learning: We study the embedding of cryptography, such as homeomorphic encryption and garbled circuits, into machine learning algorithms, in order to reduce the risk of exposing confidential personal information.
2. Active Authentication: In collaboration with Princeton University and Iowa State University, we study how to continuously and unintrusively authenticate user's identity based on his/her behavior biometrics. (CBS news: https://www.cnet.com/news/one-way-to-make-passwords-obsolete-just-keep-typing/)
3. Kernel Methods: We study the cost-effectiveness and robustness issues in kernel-based machine learning.
4. Deep Learning Applications: Scene text recognition, image dehazing, pose estimation.