Saurabh Sihag

I am an Assistant Professor in the Electrical and Computer Engineering department at State University of New York at Albany. Previously, I was a postdoctoral researcher working with Dr. Alejandro Ribeiro at the University of Pennsylvania. Broadly, I study statistical inference and machine learning approaches over graph models, both from theoretic and algorithmic perspectives with applications in network neuroscience. I had received the PhD degree in Electrical Engineering at Rensselaer Polytechnic Institute in Dec., 2020, where I was advised by Dr. Ali Tajer. My PhD thesis on Statistical Learning and Inference over Networks can be accessed here.

My research is focused on developing novel principles of machine learning and statistical inference using concepts from signal processing, learning theory, information theory, and graph theory. I have worked on a variety of research problems that include statistical learning of graph models (AISTATS, NeurIPS), state estimation in signal processing (T-IT, JSTSP, SPL), graph signal processing analyses of multimodal brain imaging data (TSIPN) and association of neuroimaging features with blood biomarkers (NatCommsMed), and adaptive graph-constrained group testing (TSP). Currently, I am investigating statistical inference using graph neural networks (VNN), with brain age prediction as a recent application (VNN_Brain_Age). A complete list of my research publications is available here.