Learning algorithms with performance guarantees through data-structural/distributional assumptions, unsupervised learning, language models (including topic models and word embeddings), matrix and tensor factorization, deep nets, sparse coding.
Efficient optimization algorithms for machine learning, non-generative unsupervised and semi-supervised learning, online convex optimization, and regret minimization in games.
Representation Learning, Foundations of Deep Learning, Reinforcement Learning, Practical algorithms with guarantees
Application areas: robotics and NLP
Reinforcement Learning, game theory, optimization, representation/meta-learning, and statistics.