I am interested in machine learning, security, privacy, game theory, blockchain and related topics. I have designed several robust learning algorithms, a scalable framework for achieving robustness for a range of learning methods, and a privacy preserving data publishing system. I am currently working on anomaly detection systems against causative poisoning attacks and malware detection with real world collected big data. I'm also working on adversarial deep learning for training generative adversarial networks (GAN) and designing robust deep neural networks against adversarial attacks. Theoretically, I utilize game theoretic analyses to model the interactions between an intelligent adversary and a machine learner, allowing defender to design robust learning strategies that explicitly account for an adversary’s optimal response. Empirically, my current research aims to scalable robust algorithms that can process massive amounts of data available for Internet-scale problems regarding specific cloud computing infrastructure to achieve large-scale secure learning for big data.