Shane Lubold
About
Research
Teaching
About
I am a PhD candidate in the statistics department at the University of Washington. I am advised by Tyler McCormick and Arun Chandrasekhar. Previously I was a Fulbright Scholar at the University of Luxembourg and before that I received my BS in math from Arizona State University. I am supported by an ARCS fellowship.
I use tools from statistical inference and machine learning to answer questions in causal inference, network analysis, and survey design. A large part of my research focuses on making large-scale surveys more accessible to researchers by making them cheaper, more accurate, and easier to run. I work on model selection for network data and learning geometric properties of data to improve down-stream tasks in inference and machine learning. I develop user-friendly software that allows researchers to better understand their data.
I am on the job market! Please feel free to email to get in touch.
Email / CV / Google Scholar / GitHub
News
- March 23, 2023: Our paper on using Aggregated Relational Data to estimate network properties was accepted to PNAS!
- December 13, 2022: Our paper on latent space geometry was accepted to JRSS Series B!
- November 18, 2022: I am giving a talk at the Pioneer Center for AI at the University of Copenhagen.
- April 27, 2022: We have posted an updated version of our paper “Consistently estimating network statistics with aggregated relational data” to arXiv.
- November 17, 2021: I will be giving a presentation at the International Society of Bayesian Analysis in June 2022 on our geometry of latent space paper!
- November 12, 2021: I am excited to announce that I received the Z.W. Birnbaum award for best general exam in the past year. Thanks to my great advisors Arun and Tyler!
- October 20, 2021: We have posted an updated version of our paper “Consistently estimating network statistics with aggregated relational data” to arXiv.
- June 17, 2021: Our pre-print on spectral goodness-of-fit tests for complete and partial network data is out today! Check out the pre-print, code, and short summary.