In 2019, I serve as a reviewer/PC member of ICLR (outstanding reviewer), AISTATS, ICML, IJCAI, GSI, ICCV, UAI, NeurIPS (top 50% reviewer)
Check our latest work on Lightlike Neuromanifolds, Occam’s Razor and Deep Learning which explains generalization of deep learning using geometric tools from general relativity.
I am attending UAI (July 22-25 2019) to present our recent work on Fisher-Bures 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 graph-structured data.