Tatsu Hashimoto

Assistant Professor of Computer Science at Stanford University

Hashimoto develops methods for making machine-learning systems reliable under real-world change. His work measures worst-case performance, exposes failures hidden by average scores and improves language-model evaluation, calibration and factuality so model confidence better reflects what it actually knows.