Raphaël Millière

Raphaël Millière

Commencing in October 2025 as the Associate Professor in Theoretical Philosophy and Computer Science in association with Jesus College, and as an Early Career Fellow in Schmidt Science’s AI2050 program.

Currently, Raphaël Millière is an Assistant Professor in the Philosophy of AI at Macquarie University. Before joining Oxford, he was a Presidential Scholar at Columbia University, and earned his DPhil from the University of Oxford. 

Prof. Millière’s research investigates foundational questions concerning the capacities and limitations of modern AI systems based on deep neural networks. In his work, he explores both first-order questions about whether these systems exhibit specific cognitive capacities, such as syntactic competence or analogical reasoning, and second-order methodological issues involved in evaluating these cognitive capacities in artificial systems. Additionally, he investigates conceptual and methodological challenges related to mechanistic interpretability, the project of reverse-engineering neural networks to uncover their underlying computations and representations. Prof. Millière’s research has been featured in various media outlets, including CNN, The Atlantic, and Wired.

Selected Publications: 

  • Millière, R. (forthcoming). Language Models as Models of Language. In R. Nefdt, G. Dupre, & K. Stanton (Eds.), The Oxford Handbook of the Philosophy of Linguistics. Oxford University Press.
  • Musker, S., Duchnowski, A., Millière, R., & Pavlick, E. (forthcoming). LLMs as Models for Analogical Reasoning. Journal of Memory and Language.
  • Millière, R. (forthcoming). Constitutive Self-Consciousness. Australasian Journal of Philosophy.
  • Millière, R., & Buckner, C. (forthcoming). Interventionist Methods for Interpreting Deep Neural Networks. In G. Piccinini (Ed.), Neurocognitive Foundations of Mind. Oxford University Press.
  • Millière, R. (2025). Normative Conflicts And Shallow AI Alignment. Philosophical Studies, 1–44.
  • Wu, Y., Geiger, A., & Millière, R. (2025). How Do Transformers Learn Variable Binding in Symbolic Programs? Forty-second International Conference on Machine Learning.
  • Millière, R. (2024). Philosophy of Cognitive Science in the Age of Deep Learning. WIREs Cognitive Science.