Mitchell Naylor is an applied machine learning professional with experience in natural language processing (NLP), statistical modeling, and deep learning. Mitch currently works as a senior applied researcher at GitHub, where he works on model improvements for GitHub Copilot.

In 2023, Mitch co-authored Applied Causal Inference, a textbook bridging the gap between causal inference theory and application. Mitch's additional publications include papers at the Interpretable Machine Learning in Healthcare (IMLH) workshop at ICML and IEEE International Conference on Bioinformatics and Biomedicine (BIBM). His textbook contributions include code and case study development for Transformers for Machine Learning: A Deep Dive (Kamath, Graham & Emara; Chapman & Hall, 2022) and Explainable Artificial Intelligence: An Introduction to Interpretable Machine Learning (Kamath & Liu; Springer 2021).