Roy Lederman, PhD

Faculty Member

Center for Neurocomputation and Machine Intelligence | Center for Neurodevelopment and Plasticity

Mathematical modeling

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.

Methods

computational modeling ; electron microscopy ; generative models ; manifold learning ; signal processing ; simulation ; statistical methods

Topics

algorithms ; artificial intelligence ; computational biology ; machine learning ; biomedical image analysis

Biography

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.

Research Contributions

Learning the geometry of common latent variables using alternating-diffusion

Applied and Computational Harmonic Analysis (2018)

Hyper-molecules: on the representation and recovery of dynamical structures for applications in flexible macro-molecules in cryo-EM

Inverse Problems (2020)

Alternating diffusion for common manifold learning with application to sleep stage assessment

IEEE (2015)