Summary:

I'm a researcher at SINTEF in the Artificial Intelligence and Analytics research group based in Oslo, where I am currently working on application of AI to industrial problems. In particular, we focus on hybrid-AI, which is a field that integrates domain knowledge, such as physics, with machine-learning. We work on a range of problems including helping industry and startups integrate machine learning into their products. Currently, my primary research focus is centered around physics-informed machine learning for time-series, which is aims at augmenting neural networks with, for example, physical priors. 

Interests:

My interests lie broadly within scientific machine learning, Python, geometric numerical analysis and computational physics. In particular, I have partaken in projects involving:

• physics-informed machine learning,

• symplectic and structure-preserving neural networks,

• energy-preserving and symplectic numerical methods for ODEs/PDEs,

• numerical methods for particles in fluids,

• discrete dynamical integrable systems,

• geometric properties of Runge-Kutta methods, and

• mimetic finite-difference methods for wave equations on curvilinear coordinates.

Education: 

• BSc, Physics, University of Western Australia, 2012-2015.

• MSc, Computational Physics, University of Western Australia, 2015-2016.

• PhD, Applied Mathematics, Norwegian University of Science and Technology, 2017-2021. 

Experience:

• Research Scientist in the AI group in the Department of Mathematics and Cybernetics in at SINTEF Digital, 2022-present

• Software Engineer for the data and analytics platform at DNB, Norway's largest bank, 2021-2022.

• Lecturer in numerical analysis and Python programming at NTNU, 2019-2020. 

• Competitive swimming coach for various clubs and high schools in Perth, 2012-2017.