Clémentine Dominé

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I am a Postdoctoral Researcher at ISTA, supported by the Cluster of Excellence (CoE) Fellowship, where I work with Pr. Marco Mondelli and Pr. Francesco Locatello.

Research

My research lies at the intersection of theoretical neuroscience and theoretical machine learning. Broadly, I aim to understand how the brain learns and builds representations to carry out complex behaviors—such as continual, curriculum, and reversal learning, as well as the acquisition of structured knowledge. I develop mathematical frameworks rooted in deep learning theory to describe adaptive and complex learning mechanisms, addressing questions that bridge machine learning and cognitive neuroscience.

Trainning

I completed my PhD at the Gatsby Computational Neuroscience Unit under the supervision of Andrew Saxe and Caswell Barry. Before that, I earned a degree in Theoretical Physics from the University of Manchester, including an exchange at the University of California, Los Angeles.

Community

Beyond my research, I am deeply involved in the academic community. I co-organize the UniReps : Unifying Representations in Neural Models workshop at NeurIPS , an event dedicated to fostering collaboration and dialogue between researchers working to unify our understanding of representations in biological and artificial neural networks. 🔵🔴

Mentoring and collaboration

Interested in working together on questions at the intersection of biological and artificial intelligence? I especially welcome students from underrepresented groups in cognitive science, neuroscience, and AI. Mentorship and collaboration are central to my work—feel free to reach out!

News

Dec 1, 2025 Heading to NeurIPS 2025 in San Diego for the UniReps workshop! Can’t wait to connect and discuss all things representation learning 🔵🔴
Aug 1, 2025 I’m excited to share that I’ll be joining Marco Mondelli and Francesco Locatello’s labs as a postdoc at ISTA Vienna starting next year. Looking forward to exploring exciting questions at the intersection of machine learning theory, representation learning, and beyond!
May 20, 2025 Got two papers accepted at ICML (2025) 🎉 🍊
  • Proca, A.M., Dominé, C., Shanahan, M. and Mediano, P.A.M., 2025. Learning dynamics in linear recurrent neural networks. Proceedings of the 42nd International Conference on Machine Learning (ICML 2025). (Oral)
  • Nam, Y., Lee, S.H., Dominé, C., Park, Y.C., London, C., Choi, W., Goring, N. and Lee, S., 2025. Position: Solve Layerwise Linear Models First to Understand Neural Dynamical Phenomena (Neural Collapse, Emergence, Lazy/Rich Regime, and Grokking). ICML 2025 Position Paper Track. [paper]
May 11, 2025 🚀 We’re excited to launch the ELLIS UniReps speaker-series, a monthly event exploring how neural models—both biological and artificial—develop similar internal representations, and what this means for learning, alignment, and reuse. Each session features a keynote by a senior researcher and a flash talk by an early-career scientist, fostering cross-disciplinary dialogue at the intersection of AI, neuroscience, and cognitive science! 🔵🔴