I am an Associate Professor in Computer Science and the Deputy Director of the Coventry University Research Centre for Computational Science and Mathematical Modelling. 

I achieved a PhD in Mathematics from Heriot Watt University, Edinburgh.  I then worked at the Universities of Glasgow and Bath, before securing a permanent position at Coventry University in April 2015.

I currently lead an ESPRC grant, Pushing Back the Doubly Exponential Wall on Cylindrical Algebraic Decomposition. I also led the EPSRC project Embedding Machine Learning in Quantifier Elimination Procedures and I was also the Coventry site PI for a large EU project on Satisfiability Checking and Symbolic Computation. 

I am currently the primary supervisor for one PDRA and five PhD students and I lead the Centres's Research Theme of Fundamental Algorithms for Artificial Intellegence.

I am on the committee of the ACM Special Interest Group for Symbolic and Algebraic Manipulation (SIGSAM) and the ongoing PC Chair of the annual International Conference on Computer Algebra in Symbolic Computation (CASC).  

See my personal academic website for further details:
https://matthewengland.coventry.domains/index.html

My research expertise is on algorithms of symbolic computation (the automation of exact symbolic mathematics) and their implementation in computer algebra systems. More specifically, I focus on algorithms of real algebraic geometry and computational logic. I have published on improved fundamental algorithms, their applications both direct (e.g. biology and economics) and in other areas of computer science (e.g. programming over complex numbers and motion planning). I am also involved in research that seeks to combine computer algebra systems with satisfiability modulo theory (SMT) solvers, and to apply machine learning techniques to improve the performance of computer algebra systems without risking mathematical correctness.

I am available to supervise PhD projects on the above, and more generally in the areas of mathematical algorithm design, machine learning applied to algorithm optimisation, and the pedagogy of programming and algorithms.