Data Theory Seminar

Date: Nov. 20, 2025

Speaker: Amit Singer

Location: Center for health science building, 43-105

Title: Mathematics of Cryo-Electron Microscopy

Abstract: Cryo-EM is a Nobel Prize winning technology for determining 3-D biological molecular structures at high resolution. Reconstruction in cryo-EM is an inverse problem that involves many different fields of mathematics including statistical inference, optimization (convex and non-convex), numerical analysis, dimension reduction, representation theory, information theory, and more.  We will discuss the mathematical and statistical foundation underlying computational methods for 3-D reconstruction, focusing on the challenges of reconstructing small size molecules and the reconstruction of flexible molecules. In passing, we will contrast modern deep learning algorithms with classical applied math and statistical methods.

Bio: Amit Singer is a Professor of Mathematics, the Director of the Program in Applied and Computational Mathematics (PACM), and a member of the Executive Committee for the Center for Statistics and Machine Learning (CSML) 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 BSc degree in Physics and Mathematics and the PhD degree in Applied Mathematics from Tel Aviv University (Israel), in 1997 and 2005, respectively. His list of awards includes SIAM Fellow (2022), the Simons Math+X Investigator Award (2017), a National Finalist for Blavatnik Awards for Young Scientists (2016), Moore Investigator in Data-Driven Discovery (2014), Simons Investigator Award (2012), 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.