Julian Chan

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Astrophysics/Machine Learning PhD Researcher

I’m a PhD student at the University of Surrey.
I develop machine learning surrogate models to predict the dynamical evolution of astrophysical systems, with a focus on supermassive binary black holes in galactic mergers. My work combines graph neural networks, neural ODEs, and symbolic regression to build interpretable, scalable models for spatiotemporal systems.

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Work Experience

Current Projects

Machine Learning for Predicting the Time Evolution of Supermassive Black Hole Binaries_ (Ongoing)

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Research Interests

I develop surrogate models for predicting the dynamical evolution of binary black holes in N-body simulations of galaxy mergers, using advanced machine learning techniques such as Neural ODEs, Graph Representation Learning, and Symbolic Regression. My current work emphasises the use of pretraining and transfer learning on related spatiotemporal systems to improve both accuracy and computational efficiency.