Assistant professor Zach Lubberts is a data scientist working on the interplay of statistics and optimization, with an emphasis on statistics on graphs. Some of his recent publications concern accurate estimation of the eigenvectors of random matrices and the capture of relevant signal in various graph models, including time series of graphs.

Lubberts earned his Ph.D. in applied mathematics and statistics from Johns Hopkins University in 2019. His dissertation research focused on the application of real algebraic geometry to the construction of multivariable tight wavelet frames for use in signal processing. He also earned his bachelor’s degree in applied mathematics and statistics and philosophy from Johns Hopkins in 2013.

As an assistant professor in the College’s Department of Statistics, Lubberts plans to continue his research in optimization and statistics, especially as it relates to network data and random matrices. He will be teaching an early-graduate-level data mining course this fall and is eager to begin new research projects with students at all levels.