
In 2019, I serve as a reviewer/PC member of ICLR, AISTATS, ICML, IJCAI, GSI, ICCV, UAI, NeurIPS

Check our latest work on Lightlike Neuromanifolds, Occamâ€™s Razor and Deep Learning which explains generalization of deep learning using geometric tools from general relativity.

I am attending UAI (July 2225 2019) to present our recent work on FisherBures Adversary Graph Convolutional Networks.
I am a machine learning scientist. My interest lies in the spectrum of unsupervised learning, Bayesian statistical learning, manifold 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 graphstructured data.