Today we’ll be speaking with Julian Morimoto about missing data, its impact on the reliability of statistical inference, and two theorems that he recently discovered using concepts from real analysis about what guarantees we can expect, at the limit of arbitrarily large data sets.

Julian has a background in math, and studied law at Harvard Law School and he speaks about the unique challenges of adopting machine learning in the legal world due to the various mechanisms of missingness in confidential documents.

If you're interested in learning more about missing data and statistical inference, here are several resources to help you get started:

• GOV2001 - Harvard: https://projects.iq.harvard.edu/gov2001/home
• One hour course by Dr. Gary Keng on missing data on YouTube: https://www.youtube.com/watch?v=qlPs8Ioa56Y
• Julian’s paper: https://arxiv.org/abs/2112.09275v4
• Statistical Analysis with Missing Data book: https://www.amazon.com/Statistical-Analysis-Missing-Roderick-Little/dp/0471183865?