Rate My Professor Simon Shaw

Professor Simon Shaw is a Professor in the Department of Mathematics in the College of Engineering, Design and Physical Sciences at Brunel University London. He belongs to the Applied and Numerical Analysis Research Group and is a member of the Structural Integrity theme of the Institute of Materials and Manufacturing, the Centre for Assessment of Structures and Materials under Extreme Conditions, and the Centre for Mathematical and Statistical Modelling. His career began as a craft mechanical engineering apprentice, which he left due to redundancy to complete a mechanical engineering degree via night school while working odd jobs. He then designed desktop dental X-ray processing machines before retraining in computational mathematics and joining Brunel University London. Currently, he serves as Interim Joint (50%) Head of Department since November 2022.

Shaw's research focuses on computational science, engineering, and mathematics, particularly finite element and related methods in space and time for partial differential equations arising in continuum mechanics, with emphasis on dispersive media such as viscoelastic polymers and lossy dielectrics exhibiting memory effects. He investigates deep neural networks and machine learning for inverse problems using real or virtual training data, applied to noninvasive screening for coronary artery disease, which is being commercialized via a spin-out company. He supervises PhD projects on finite element and discrete time methods for PDEs with memory in biotissues, polymers, and electromagnetism of lossy dielectrics, as well as deep learning for inverse scattering problems. His teaching includes Mathematics of Deep Learning, Analysis 1, Numerical and Variational Methods for Partial Differential Equations, and Applied Engineering Mathematics. Shaw has authored over 30 research papers, including 'A Priori Analysis of a Symmetric Interior Penalty Discontinuous Galerkin Finite Element Method for a Dynamic Linear Viscoelasticity Model' (2023, with Y. Jang, Computational Methods in Applied Mathematics), 'Approximate Fourier series recursion for problems involving temporal fractional calculus' (2022, with J.R. Whiteman, Computer Methods in Applied Mechanics and Engineering), 'Finite element approximation and analysis of a viscoelastic scalar wave equation with internal variable formulations' (2022, with Y. Jang, Journal of Computational and Applied Mathematics), 'A priori error analysis for a finite element approximation of dynamic viscoelasticity problems involving a fractional order integro-differential constitutive law' (2021, with Y. Jang, Advances in Computational Mathematics), and 'An a priori error estimate for a temporally discontinuous Galerkin space-time finite element method for linear elasto- and visco-dynamics' (2019, Computer Methods in Applied Mechanics and Engineering). In 2024, he delivered his inaugural lecture 'From Machine Tools to Machine Learning'.