About
Dr. Roy Lederman is an Assistant Professor at the Department of Statistics and Data Science, Yale University. He is affiliated with the Quantitative Biology Institute (QBio), the Applied Math Program, the Institute for Foundations of Data Science (FDS), and the Wu Tsai Institute (WTI). He was awarded the Sloan Research Fellowship (2023). He previously held a Gibbs Assistant Professorship at Yale (2014-2015) and a postdoc at Princeton University (2015-2018).
- Education: PhD in Applied Mathematics, Yale University (2014); dual BSc in Physics and Electrical Engineering, Tel-Aviv University.
- Teaching: Courses include Computational Tools for Data Science, Signal Processing, and Mathematical Machine Learning.
Research Areas: Dr. Lederman works at the intersection of computational biology, structural biology, Bayesian inference, numerical analysis, and machine learning. His recent work focuses on cryo-EM and hyper-molecules for studying molecular heterogeneity, alternating diffusion for common variable recovery, and Zernike polynomials for 3D imaging. He also develops Hamiltonian Monte Carlo methods and randomized DNA sequencing algorithms.
Publications Trends: His publications (15 most recent) emphasize structural biology and cryo-EM applications, machine learning (Bayesian deep learning, diffusion maps), numerical analysis (Fourier/Laplace transforms), and computational biology (DNA sequencing algorithms). Key sub-fields include heterogeneity analysis, manifold learning, Hamiltonian Monte Carlo, and Zernike polynomials.
- Scientific Awards: Sloan Research Fellow (2023)
Dr. Lederman actively mentors graduate students and postdocs at Yale, and co-organizes the One World Cryo-EM seminar series. His lab develops open-source software (e.g., prolate function implementation) and explores theoretical bounds on transforms and common variable recovery in multi-sensor experiments.
