Shane Lubold

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I am a Research Mathematical Statistician in the Missing Data and Observational Data Modeling Group in the Center for Statistical Research and Methodology at the U.S. Census Bureau. I build machine learning models to reduce non-response bias.

I recieved my PhD in statistics from the University of Washington in June 2023. I was advised by Arun Chandrasekhar at Stanford University and Tyler McCormick at UW. Previously I was a Fulbright Scholar at the University of Luxembourg and before that I received my BS in math from Arizona State University. At UW, I was supported by an ARCS fellowship.

My work in graduate school focused on questions in causal inference, network analysis, and survey design. A large part of my research focused on making large-scale surveys more accessible to researchers by making them cheaper, more accurate, and easier to run. My work also focused on building statistical models for network data. I also worked on model selection for network data and learning geometric properties of data to improve down-stream tasks in inference and machine learning.

Email / CV / Google Scholar / GitHub

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