Applying machine learning to the flagellar motor for biosensing. - Tom J. Zajdel; Andrew Nam; Jove Yuan; Vikram R. Shirsat; Behzad Rad; Michel M. Maharbiz (EMBC, 2018)

Resource-Aware Pareto-Optimal Automated Machine Learning Platform. - Yao Yang; Andrew Nam; Mohamad M. Nasr-Azadani; Teresa Tung (CoRR, 2020)

Racial disparities in automated speech recognition. - Allison Koenecke; Andrew Nam; Emily Lake; Joe Nudell; Minnie Quartey; Zion Mengesha; Connor Toups; John R. Rickford; Dan Jurafsky; Sharad Goel (Proc. Natl. Acad. Sci. USA, 2020)

Identifying Posts from Reddit Talking About ADRD Caring Experiences Using Machine Learning. - Congning Ni; Jonathan Hughes; Andrew Nam; Zhijun Yin (AMIA, 2021)

Show and tell: Learning causal structures from observations and explanations. - Andrew Nam; Christopher Hughes; Thomas Icard; Tobias Gerstenberg (CogSci, 2023)

Discrete, compositional, and symbolic representations through attractor dynamics. - Andrew Nam; Eric Elmoznino; Nikolay Malkin; Chen Sun 007; Yoshua Bengio; Guillaume Lajoie (CoRR, 2023)

Using the Tools of Cognitive Science to Understand Large Language Models at Different Levels of Analysis. - Alexander Ku; Declan Campbell; Xuechunzi Bai; Jiayi Geng; Ryan Liu 001; Raja Marjieh; R. Thomas McCoy; Andrew Nam; Ilia Sucholutsky; Veniamin Veselovsky; Liyi Zhang; Jian-Qiao Zhu; et al. (CoRR, 2025)

Causal Head Gating: A Framework for Interpreting Roles of Attention Heads in Transformers. - Andrew Nam; Henry Conklin; Yukang Yang; Thomas L. Griffiths 001; Jonathan D. Cohen 003; Sarah-Jane Leslie (CoRR, 2025)

Understanding Task Representations in Neural Networks via Bayesian Ablation. - Andrew Nam; Declan Campbell; Thomas L. Griffiths 001; Jonathan D. Cohen 003; Sarah-Jane Leslie (CoRR, 2025)