Zachary Lubberts

Zachary Lubberts is an Assistant Professor in the Department of Statistics at the University of Virginia. His research spans statistics, machine learning, and applied mathematics, with a focus on network analysis, spectral methods, and wavelet frame construction. He received his PhD in Applied Mathematics and Statistics from Johns Hopkins University in 2019, advised by Youngmi Hur, and holds undergraduate degrees in Applied Mathematics/Statistics and Philosophy from JHU.

• PhD: Johns Hopkins University (2019)
• MSc: Johns Hopkins University
• BSc: Johns Hopkins University
• BA: Johns Hopkins University

His work explores statistics on graphs, including spectral clustering in multilayer networks, time series analysis of dynamic networks using Euclidean mirrors, and edge-attributed network inference via line graph spectral decompositions. He also investigates nonseparable wavelet frames for directional signal processing and optimization techniques for random matrix analysis.

Recent publications in the Journal of the American Statistical Association and Bernoulli highlight his contributions to multilayer network clustering, changepoint detection in network time series, and curvature-based clustering. He received the CosmicAI Seed Funding grant for work on merger trees.

• Teaches courses: Statistical Machine Learning, Optimization, Probability, Real Analysis
• Advises PhD student Ga Ming (Angus) Chan and undergraduate researcher Adriel Barretto