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Research Fellow
Theoretical Physics, University of Oxford
UKRI Horizon Europe Guarantee Marie Skłodowska-Curie Fellowship Award
Project: Phenomenology of Deep Learning |

News/Recent and Upcoming Events

  • April: Our paper "An exactly solvable model for emergence and scaling laws" is on arXiv.

  • March-June: I'm currently participating in the AI Alignment course (AI Safety Fundamentals) by BlueDot Impact. Feel free to reach out if you'd like to exchange ideas or explore potential collaborations.

Home: Welcome

I am a physicist seeking ways to understand generalization in deep learning. Towards this goal, I study AI as a scientific discipline, where both empirical validity and explainable methodologies are essential. For instance, see [here] for our work on generalization and optimization in physics-informed neural networks, and see [here] for our work on double descent and online/offline correspondence in similarity learning. 


Previously, I explored the construction of models to explain small- and large-scale phenomena in the universe. I worked on the interplay between particle physics and cosmology, learning from the early universe how to build and interpret our models of particle physics. See [here] for all publications. 



I'm a Research Fellow at Oxford Theoretical Physics, where I'm exploring the interplay between Machine Learning and Physics. I'm hosted at Ard Louis group. Before Oxford, I worked as a Research Scientist at IBM Research UK. Previously, I was at ICTP in Trieste, DESY Theory Group in Hamburg, CTP at MIT, University of São Paulo, and University of Campinas.


For more details, please see my [CV].

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