Miles Cranmer
Miles Cranmer is an Assistant Professor in Data Intensive Science at the University of Cambridge. He received his PhD in Astrophysics from Princeton University and his BSc in Physics from McGill University.
Miles is interested in the automation of scientific research with machine learning. His current focus is the development of interpretable machine learning methods, especially symbolic regression, as well as applying them to multi-scale dynamical systems.
Talks:
18:30 UTC
Interpretable Machine Learning with SymbolicRegression.jl
SymbolicRegression.jl is a state-of-the-art symbolic regression library written from scratch in Julia using a custom evolutionary algorithm. The software emphasizes high-performance distributed computing, and can find arbitrary symbolic expressions to optimize a user-defined objective – thus offering a very interpretable type of machine learning. SymbolicRegression.jl and its Python frontend PySR have been used for model discovery in over 30 research papers, from astrophysics to economics.
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