I study machine learning, a sub-discipline of artificial intelligence. My general interest lies in the spectrum of unsupervised learning, Bayesian statistical learning, manifold/metric learning, and deep learning. I formulate statistical learning problems from the perspective of information geometry (think of general relativity as the curvature of spacetime; information geometry is about the curvature of information), which tackles the essence of information using elegant mathematical tools. A goal of my research is to build a geometric theory of intelligence. I like simple and profound methodologies that are (anti-)intuitive, and therefore my works are more conceptual than practical. Recently I have been focusing on machine learning on graph-structured data.