Saurabh Sihag

Dr. Saurabh Sihag is an Assistant Professor in the Department of Electrical and Computer Engineering at the University at Albany. He received his PhD degree from Rensselaer Polytechnic Institute in 2020 and his Bachelor’s and Master’s degrees in Electrical Engineering from Indian Institute of Technology, Kharagpur in 2016. He worked as a postdoctoral researcher at the University of Pennsylvania (2021-2024) and a research intern in computational neuroscience and machine learning at IBM Research in 2018 and 2019, and is a recipient of the J. Baliga fellowship and Charles M. Close ’62 Doctoral Prize for his doctoral dissertation. 

Research Interests

Dr. Sihag’s research interests include statistical inference and machine learning over graph-structured data. His current research program focuses on novel theoretical principles using signal processing, statistics, graph theory and learning theory for optimal information processing in various application domains, with a specific focus on the computational aspects of network neuroscience.

Research/Specialty Areas

Signal processing, machine learning, graph theory, network neuroscience

Recent Publications

For a complete list of publications see his Google scholar page.

Selected Publications

• Khalafi, S., Sihag, S., and Ribeiro, A., “Neural tangent kernels motivate graph neural networks with cross-covariance graphs”, International Conference on Machine Learning (ICML), 2024.
• Sihag, S., Mateos, G., McMillan, C., and Ribeiro, A., “Transferability of coVariance Neural Networks”, IEEE Journal of Selected Topics in Signal Processing, 2024.
• Sihag, S., Mateos, G., McMillan, C., and Ribeiro, A., “Explainable brain age prediction using coVariance neural networks”, Conference on Neural Information Processing Systems (NeurIPS), 2023.
• Sihag, S., Mateos, G., McMillan, C., and Ribeiro, A., “coVariance neural networks”, Conference on Neural Information Processing Systems (NeurIPS), 2022.