Naomi Nguyen is a machine learning practitioner whose work emphasizes the unglamorous but decisive parts of applied ML: rigorous model evaluation and the imbalanced class problem. The failure modes she worries about most are the ones that quietly sink real systems, including unusual events that never appear in training data, model overfitting, and outdated modeling caused by the underlying distribution of new data shifting over time. That focus on robustness reflects how she thinks about machine learning generally: a model is only as good as the evidence behind it.

Colleagues know Naomi as a fast and curious learner, an eager problem-solver, and a firm believer in communication, transparency, and constructive criticism. She treats those not as soft skills but as working practices that make technical projects succeed.

Naomi also enjoys teaching mathematics and statistics, as well as having discussions on new fields, and she brings that same openness and clarity to everything she works on.