Matt DeButts

Matt DeButts is a PhD candidate in Stanford University’s Department of Communication and a Stanford Data Science PhD scholar whose research examines authoritarian influence on global media production, Chinese digital media, political communication, and information controls using computational methods, large-scale datasets, and qualitative interviews.

AI Tools Target Labeling, Simulation, and Scaling Bottlenecks in Research

At Stanford’s second AI+Science lightning-talk session, three researchers presented AI less as a general-purpose scientific shortcut than as infrastructure for specific measurement problems. Matt DeButts argued that PRC-linked patronage can reshape Chinese-language media markets by helping already favorable outlets survive; Samuel Young showed how self-supervised learning can extract particle structure from unlabeled detector data; and Benjamin Dodge described using AI-scale computation to make Gaussian process priors practical for 3D maps of Milky Way dust. The shared claim was that AI’s value depended on a sharply defined bottleneck: too many articles to label, too few reliable detector labels, or too large an inference problem for conventional computation.

Stanford HAIMay 15, 20268 min read