Zachary Lubberts

Hi there!

I'm a data scientist. In my case, that means: some optimization, some programming, some signal processing, some probability and statistics, and a lot of linear algebra. My recent work has focused on statistics on graphs, and natural language processing.

I finished my PhD at Johns Hopkins University in 2019, advised by Youngmi Hur (now at Yonsei University in Seoul, Korea). In my dissertation, Generating Tight Wavelet Frames from Sums of Squares Representations, I used tools from real algebraic geometry to construct nonseparable multivariate tight wavelet frames with many vanishing moments. These frames can be used to better capture signals with strong directional content that is not aligned with the main coordinate axes, useful for compression, denoising, or signal extraction. Before that, I studied Philosophy and Applied Math and Statistics in undergraduate, also at JHU (2010-2013).

I teach courses on a wide range of topics, including mathematical computing, optimization, probability and statistics, and real analysis, to students at all levels of undergraduate, masters, and PhD study, and in several different modalities.

Recent Updates

• Congratulations to Siyi Gao on the Bynum Family Graduate Fellowship! Announcement here 

• "Vertex misalignment and changepoint localization in network time series" is now available on arXiv: arXiv:2604.20072 

• "Recovering manifold structure in LLM responses through a joint Euclidean mirror" is now available on arXiv: arXiv:2604.07011 

• "KRAFTY: Khatri-Rao Framework for Joint Cluster Recovery" is now available on arXiv: arXiv:2603.04608 

• "Procrustes problems on random matrices" is now available on arXiv: arXiv:2510.05182 

• "Joint Spectral Clustering in Multilayer Degree-Corrected Stochastic Blockmodels " is now available at the Journal of the American Statistical Association!

• I am very grateful to have received a CosmicAI Seed Funding grant for my work on merger trees: Announcement here