Hannah Sheahan

I am an AI researcher at OpenAI, focused on building safe and capable models from data. Before OpenAI, I was a Research Engineer at Google DeepMind where I worked on human-AI interaction in LLMs and helped to build Gemini.

Before switching to AI research, I researched human learning. I did my PhD at The University of Cambridge in the Computational & Biological Learning Lab, supervised by Daniel Wolpert, and a postdoc at The University of Oxford with Chris Summerfield.

My PhD and postdoc were in cognitive science. I was interested in motor learning, generalisation and the effects of context and task structure on learning in humans (and later, neural networks). I also hold a BEng (hons) in engineering from the University of Auckland in NZ.

Publications

Thompson, J, Sheahan, H., Dumbalska, T., Sandbrink, J., Piazza, M., Summerfield, C. (2024), Zero-shot counting with a dual-stream neural network model. ArXiv

Gemini team (2024), Gemini 1.5: Unlocking multimodal understanding across millions of tokens of context. ArXiv

Gemini team (2023), Gemini: a family of highly capable multimodal models. ArXiv

Muhle-Karbe, P.*, Sheahan, H.*, Pezzulo, G., Spiers, H., Chien, S., Schuck, NW.*, Summerfield C.* (2023), Goal-seeking compresses neural codes for space in the human hippocampus and orbitofrontal cortex. Neuron

Dasgupta, I.*, Lampinen, A.*, Chan, S., Sheahan, H., Creswell, A., Kumaran, D., McClelland, J., Hill, F. (2023), Language models show human-like content effects on reasoning. ArXiv

Bakker, M.*, Chadwick, M.*, Sheahan, H.*, Tessler, MH., Campbell-Gillingham L., Balaguer, J., McAleese, N., Glaese, A., Aslanides J., Botvinick M., Summerfield. C. (2022), Fine-tuning language models to find agreement among humans with diverse preferences. NeurIPS.

Bondi, L., Koster, R., Sheahan, H., Chadwick, M., Bachrach, Y., Cemgil, T., Paquet U., Dvijotham, K. (2022), Role of human-ai interaction in selective prediction. AAAI. 36(5)

Hawthorne, C., Jaegle, A., Cangea, C., Borgeaud, S., Nash, C., Malinowski, M., Dieleman, S., Vinyals, O., Botvinick, M., Simon, I., Sheahan, H., Zeghidour, N., Alayrac, JB., Carreira, J., & Engel. J. (2022), General-purpose, long-context autoregressive modeling with perceiver ar. ICML.

Sheahan, H.*, Luyckx, F.*, Nelli, S., Teupe, C., & Summerfield C. (2021), Neural state space alignment for magnitude generalization in humans and recurrent networks. Neuron. 109 (7), 1214-1226

Albert, S., Jang, J., Sheahan, HR., Teunissen, L., Vandevoorde, K., & Shadmehr, R. (2021), An implicit memory of errors limits human sensorimotor adaptation. Nature Human Behaviour.

Summerfield, C., Luyckx, F., & Sheahan, H. (2020), Structure learning and the posterior parietal cortex. Progress in Neurobiology. 184

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