My primary research interests lie in the general areas of Machine Learning, Artificial Intelligence, Natural Language Processing, as well as ML systems, computer vision, healthcare, and other application domains.

In particular, I'm interested in principles and methodologies of Panoramic Learning (paper)—training AI agents with ALL types of experiences, ranging from data instances (NeurIPS), structured knowledge (ACL, NeurIPS), constraints, to rewards, adversaries (NeurIPS), lifelong interplay, etc. To this end, I've been studying a standardized ML formalism (``Standard Model'' of ML) for systematic understanding, unifying, and generalizing a wide range of ML paradigms (e.g., supervised, unsupervised, active, reinforcement, adversarial, meta, lifelong learning).

On this basis, I develop methods and tools for Composable ML that enables easy composition of ML solutions (Texar, ASYML, as part of the open-source consortium CASL); and rich applications for controllable text generation (ICML) and others.