Title: Geometric Methods for Network Science and Computational Computer Vision

Abstract: In this talk, we will present recent advances in discrete geometry and optimal mass transport as applied to network science, computational computer vision, and machine learning. To motivate necessary mathematical ingredients, we begin by revisiting classical vision problems in segmentation, shape analysis, shape registration, and pose estimation. Concepts such as curvature and its connection to not only system robustness, but also in shape reconstruction and related vision tasks will help lay the foundation for a variety of applications in control-based tracking, mesh analysis, and machine learning. From this, we then shift our attention towards how such concepts can be applied in networks to elucidate functional properties of complex systems. Applications in cancer targeted therapy, drug design, congestion, to even the development of economic indicators for financial risk will be discussed.

This talk is designed for a graduate level audience who are seeking PhD research opportunities in networks, computer vision, machine learning, and systems biology.

Bio: Romeil Sandhu is an Assistant Professor in the Biomedical Informatics Department and an affiliated/adjunct faculty member in the Computer Science and Applied Mathematics & Statistics Department. Currently, he directs the Laboratory for Imaging, Networks, and Control (LINC) at Stony Brook University. He received his B.S. M.S., and Ph.D. from the Georgia Institute of Technology in 2006, 2009, and 2010 respectively.