"My main goal is to discover the principles of learning from data and use them to develop algorithms that can learn like living beings."
My current focus is on new algorithms for computers to autonomously learn to perceive, act, and reason throughout their lives.
I work on problems in several areas of machine learning, such as approximate inference, deep learning, reinforcement learning, active learning, online learning, and reasoning in computer vision. In my recent works, I have worked on ideas from a wide range of fields, such as, optimization, Bayesian statistic, information geometry, signal processing, and control systems.
For more of my research activites, see the team ApproxBayes webpage.