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DR Andrew Cullen


  • Machine Learning (Machine learning, signal process, GIS, prediction)
  • Nonlinear equations (Nonlinear analysis, equations, modeling, dynamical systems, hamiltonian systems)
  • Numerical methods (Spectral, numerical, nonlinear, differential equations, ODE, BVP, PDE, boundary value, ordinary differential, partial differential)



  • Andrew Cullen received a PhD in Applied and Computational Mathematics from Monash University in 2018, for his work in developing a novel, class leading algorithm for solving nonlinear, variable coefficient differential equations, with a focus upon applications to Fluid Mechanics and Turbulence. He also holds Bachelors degrees in Aerospace Engineering (Honours) and Science, and an additional Honours degree in Applied Mathematics, also from Monash University. While studying, he held an Equity and Excellence scholarship, an Australian Postgraduate Award, and received the Leo Gleeson prize for applied mathematics, and the award for the best presentation at the Computational Techniques and Applications Conference (CTAC) in 2016. His research interests span algorithm design, numerical analysis, turbulent and atmospheric fluid dynamics, and engineering design. Currently he is considering applications of machine learning to predicting complex events in space and time, and the use of machine learning to design structures. Outside of academia he has worked as a data scientist for a technology company, and as a consultant focusing upon the applications of mathematics to sports.   



Education and training

  • PhD (Sci), Monash University 2018
  • BAE(Hons)/BSc, Monash University 2014
  • BSc (Hons), Monash University 2011

Awards and honors

  • Leo Gleeson prize for Applied Mathematics, Monash Univeristy, 2011
  • Excellence and Equity Scholarship, 2007



Available for supervision

  • N