Liza Levina received her Ph.D. in Statistics from UC Berkeley in 2002 and joined the University of Michigan the same year. She received the junior Noether Award from the ASA in 2010 and was elected a member of ISI in 2011.
Dr. Levina's research interests include networks, high-dimensional data, and sparsity. She has worked on estimating large covariance matrices, graphical models, and other topics in inference for high-dimensional data. She also works on statistical inference for network data, including problems of community detection and link prediction. Her research covers methodology, theory, and applications, especially to spectroscopy, remote sensing and, in the past, computer vision.
Can M. Le, Elizaveta Levina, Roman Vershynin. “Sparse random graphs: regularization and concentration of the Laplacian.” (2015)
Yuan Zhang, Elizaveta Levina, Ji Zhu. “Detecting Overlapping Communities in Networks Using Spectral Methods.” (2014)
Arash A. Amini, Elizaveta Levina. “On semidefinite relaxations for the block model.” (2014)
Can M. Le, Elizaveta Levina, Roman Vershynin. “Optimization via Low-rank Approximation for Community Detection in Networks.” (2014)
Arash A. Amini, Elizaveta Levina, Kerby A. Shedden. “Structured functional regression models for high-dimensional spatial spectroscopy data.” (2013)