Lederman's research is in mathematical modeling, computational mathematics, statistical inference, and the geometry of data. Recent work includes algorithms for analyzing cryo-electron microscopy data to understand biological processes at the molecular and subcellular levels.
Roy Lederman received a BSc in Physics and Electrical Engineering from Tel Aviv University and a PhD in Applied Mathematics from Yale University. After a postdoc at Princeton, he joined Yale's Department of Statistics and Data Science. His group is located at the Institute for Quantitative Biology.
Applied and Computational Harmonic Analysis (2018)
Hyper-molecules: on the representation and recovery of dynamical structures for applications in flexible macro-molecules in cryo-EMInverse Problems (2020)