My name is Hans Hanley (汉斯·汉隶) and I am a Member of Technical Staff at Microsoft AI. I am broadly interested in AI safety and truthfulness, disinformation, networks, and natural language processing.
I was formerly a Computer Science Ph.D. candidate at Stanford University researching in the Empirical Security Research Group and supported by the Meta PhD Research Fellowship, the National Science Foundation Graduate Research Fellowship and the Stanford University EDGE Fellowship. I completed two Masters' degrees in Computer Science and in Statistics with the Daniel M. Sachs Scholarship at the University of Oxford. I completed my undergraduate degree in Electrical Engineering at Princeton University.
Outside of research, I did disinformation reporting with the Atlantic Council's Digital Forensic Research Lab.
I have a blog, The Margin of Error, where I talk about my research, computational looks at different economic and political issues, and a ton of other topics. Subscribe on Substack, or via this form.
Sub-Standards and Mal-Practices received a Best Paper Honorable Mention at CSCW 2025.
AwardHierarchical Level-Wise News Article Clustering via Multilingual Matryoshka Embeddings accepted to the Main Conference at ACL 2025.
PaperI defended my thesis, Narrative Networks: Investigating Patterns of Influence and Propaganda across International News Outlets.
NoteAcross the Firewall accepted as a Brief Report to PNAS.
PaperNamed a Rising Star in Data Science — Stanford / UC San Diego / University of Chicago.
AwardAwarded the Meta PhD Research Fellowship in Computational Social Science.
AwardIn this work, we propose a new platform-agnostic framework that enables accurate reconstruction of cross-platform social graphs by uncovering latent narratives, revealing that a small group of bridge users drives most narrative migration between platforms like Truth Social and X, with broad implications for content moderation and policy.
In this work, we utilize Matryoshka-based learning techniques to build hierarchical embeddings that can differentiate news articles at different levels of granularity.
In this work, we examine the role of online misinformation in sparking political incivility and toxicity on the social media platform Reddit’s online communities or subreddits.
In this work, we perform narrative analysis on 29.0M different articles from 2022 and 2023 across 4,076 different low, mixed, and highly reliable websites.
We track the origin and spread of narratives from foreign news websites onto the Chinese social media platform Weibo.