website picture
Erin Grant
she / her

I am a Senior Research Fellow at the
Sainsbury Wellcome Centre & Gatsby Computational Neuroscience Unit
at University College London (UCL).

Research summary: I study prior knowledge and learning mechanisms in minds, brains, and machine using a combination of behavioral experiments, computational simulations, and analytical techniques. See my research page for more.

Recent News

active / ongoing

🔬 We’re looking for pre-doctoral Project Research Interns at the Gatsby! We review applications 4 times a year and the start date is flexible. We especially encourage applications from students who have faced barriers to participation in research. See the eligibility criteria and apply here. ⬅️

Jul 2024

📚 Like last year, I’ll be co-teaching a module on dynamical systems with Clémentine Dominé as part of the Gatsby Bridging Programme.

I’m lecturing at the OxML Summer School in Oxford…

Jun 2024

…and the ABC Summer School on NeuroAI at the University of Amsterdam!

May 2024

🧠💭🤖 We’re running a workshop on Representational Alignment at ICLR! Please consider attending on May 11th (the ICLR workshops day) in Vienna to see the lineup of invited talks and contributed presentations.

I am serving as a DEI Chair at ICLR 2024 alongside Rosanne Liu. Send us your thoughts on inclusion efforts, Tiny Papers, and anything else; we read every email!

Jan 2024

I’m now an Action Editor for Transactions on Machine Learning Research.

Recent Publications

For a complete list, see my research page or my CV.

The transient nature of emergent in-context learning in transformers.
The transient nature of emergent in-context learning in transformers.
Aaditya K. Singh*, Stephanie C.Y. Chan*, Ted Moskovitz, Erin Grant, Andrew Saxe, Felix Hill.
In Advances in Neural Information Processing Systems (NeurIPS), 2023.
Bayes in the age of intelligent machines
Bayes in the age of intelligent machines
Thomas L. Griffiths, Jian-Qiao Zhu, Erin Grant, R. Thomas McCoy.
arXiv: 2311.10206.
Gaussian process surrogate models for neural networks.
Gaussian process surrogate models for neural networks.
Michael Y. Li, Erin Grant, Thomas L. Griffiths.
In Proceedings of the Conference on Uncertainty in Artificial Intelligence (UAI), 2023.
Distinguishing rule- and exemplar-based generalization in learning systems.
Distinguishing rule- and exemplar-based generalization in learning systems.
Ishita Dasgupta*, Erin Grant*, Thomas L. Griffiths.
In Proceedings of the International Conference on Machine Learning (ICML), 2022.

Recent Service

2024
Co-organizer
2024—
Action Editor
TMLR
2022—
Area Chair
AutoML, ICLR, NeurIPS
2022—2024
Director
2022–2024
Mentor & Placement Host