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Gillian K. Hadfield on Muck Rack

Gillian K. Hadfield

East Coast United States
As seen in: MSN, Financial Times, Nature, The Washington Post, arXiv, Los Angeles Times, OUPblog, TechCrunch, WIRED en Español, WIRED Magazine and
AI policy and alignment; integrating law, economics & computer science to build normatively competent AI that knows how to play well with humans
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Articles

A unified machine-learning framework for ab initio multiscale modeling of liquids

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By Gillian K. Hadfield, Neel Guha, Andy Zhang
| PNAS Verified
P. L. Geissler, C. Dellago, D. Chandler, J. Hutter, M. Parrinello, Autoionization in liquid water. Science 291, 2121–2124 (2001). C. Zhang, Z. Yu, R. Car, A. Selloni, Tuning water dissociation at oxide-electrolyte interfaces with electric fields. Proc. Natl. Acad. Sci. U.S.A. 122, e2505929122 (2025). H. Zhang, V. Juraskova, F. Duarte, Modelling chemical processes in explicit solvents with machine learning potentials. Nat. Commun. 15, 6114 (2024). S. Perkin, T. Albrecht, J.
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The social-class gap in negotiation: Lower-class individuals negotiate less and face more backlash

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By Michele J. Gelfand, Gillian K. Hadfield, Neel Guha
| PNAS Verified
Significance Economic inequality continues to widen in the United States and globally, yet the organizational processes that reproduce social stratification remain insufficiently understood. This research identifies negotiation propensity as a key behavioral pathway that exacerbates class disparities: lower-class individuals are less likely to initiate negotiations, a pattern associated with persistent inequalities in pay and advancement.
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Context-aware multimodal AI navigates hidden pathways in five centuries of art evolution

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By Byunghwee Lee, Gillian K. Hadfield, Neel Guha
| PNAS Verified
Context-aware multimodal AI navigates hidden pathways in five centuries of art evolution Edited by Diana Greenwald, Isabella Stewart Gardner Museum, Boston, MA; received July 7, 2025; accepted May 19, 2026, by Editorial Board Member Paul DiMaggio Significance We demonstrate how multimodal AI provides quantitative insights into data-driven art history by projecting 500 y of Western paintings into CLIP’s high-dimensional semantic spaces.
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