Roy Lederman

Roy Lederman is an Assistant Professor in the Department of Statistics and Data Science at Yale University. He is affiliated with the Quantitative Biology Institute (QBio), Applied Math Program, Institute for Foundations of Data Science (FDS), and the Wu Tsai Institute (WTI). A Sloan Research Fellow (2023), his work spans cryo-electron microscopy (Cryo-EM), Bayesian inference, numerical analysis, and computational biology. Prior to Yale, he was a postdoc at Princeton University and held a Gibbs Assistant Professorship at Yale.

Education includes a PhD in Applied Mathematics from Yale (advised by Vladimir Rokhlin and Raphy Coifman), with earlier degrees in Physics and Electrical Engineering from Tel-Aviv University. His research focuses on mathematical and algorithmic challenges in data science, particularly in recovering molecular structures from Cryo-EM data and developing methods for heterogeneous systems.

Notable contributions include hyper-molecule models for dynamic structures, alternating diffusion for multi-sensor data, and Bayesian methods for inference on manifolds. His work bridges theoretical mathematics and computational tools, with applications in structural biology and signal processing.

Recent publications highlight advancements in manifold learning, stochastic gradient descent for Cryo-EM refinement, and integrating molecular models with Cryo-EM data. He actively mentors postdocs and graduate students, emphasizing interdisciplinary approaches to data science challenges.