The popular representation of datasets and networks as undirected weighted graphs is instrumental for many purposes such as clustering, dimensionality reduction, semi-supervised learning, and more. In this talk we will explore several instances in which endowing the edges with vectors or transformations is beneficial for certain applications in computer vision, structural biology, and manifold learning.
Amit Singer is a Professor of Mathematics and a member of the Executive Committee of the Program in Applied and Computational Mathematics (PACM) at Princeton University. He joined Princeton as an Assistant Professor in 2008. From 2005 to 2008 he was a Gibbs Assistant Professor in Applied Mathematics at the Department of Mathematics, Yale University. Singer received the BS degree in Physics and Mathematics and the PhD degree in Applied Mathematics from Tel Aviv University (Israel), in 1997 and 2005, respectively. He was awarded the Moore Investigator in Data-Driven Discovery (2014), the Simons Investigator Award (2012), the Presidential Early Career Award for Scientists and Engineers (2010), the Alfred P. Sloan Research Fellowship (2010) and the Haim Nessyahu Prize for Best PhD in Mathematics in Israel (2007). His current research in applied mathematics focuses on theoretical and computational aspects of data science, and on developing computational
methods for structural biology.