David Dye is a Professor of Metallurgy in the Department of Materials. He develops alloys for jet engines, nuclear and caloric materials so as to reduce fuel burn and avoid in-service failure. This involves crystallography (vectors and transformation matrices) and techniques like neutron and synchrotron X-ray diffraction and electron microscopy at the atomic scale. These give rise to 'big data' analysis problems associated simply with the amounts of data we can now collect. His Phd and undergraduate degrees were from Cambridge University in 1997 and 2000; he joined Imperial in 2003. He also teaches introductory mathematics - errors and data analysis, and has won student-led awards for innovation in teaching.
Mathematics for Machine Learning: Linear Algebra
Mathematics for Machine Learning: Multivariate Calculus