Email ben kent at live dot co dot uk
Website https://benmkent.github.io/
GitHub https://github.com/benmkent/

Academic Experience

February 2024 – Present: Postdoc at IMATI-CNR, Pavia, Italy

Development of novel sparse grid polynomial approximation algorithms in the context of uncertainty quantification.

September 2019 – November 2023: PhD in Numerical Analysis at The University of Manchester, UK

Supervised by Professor Professor Catherine Powell and Professor David Silvester.
Industry sponsored ICASE project with IBM Research UK.
Thesis: Efficient Approximation of Parametric Parabolic Partial Differential Equations. [PDF]
Investigated adaptive-in-time sparse-grid stochastic collocation approximation of a parametric time-dependent advection–diffusion problem.
Techniques considered include finite element method, adaptive timestepping (local error control) and sparse grid polynomial approximation.
Hierarchical and residual error estimation strategies developed in the context of novel adaptive approximation algorithms.
Completed courses in Uncertainty Quantification, Functional Analysis, Approximation Theory and Finite Element Analysis, Adaptive Finite Element Methods.
Other topics studied include UQ techniques (Monte Carlo, stochastic collocation, stochastic Galerkin methods), a priori and a posteriori error estimation for FEM approximations of elliptic problems and Bayesian inverse problems.

September 2012 – July 2015: Mathematics and Physics BSc, First-Class Honours at The University of Warwick, UK

September 2010 – July 2012: The College of Richard Collyer, Horsham, UK

A Levels: Mathematics A*, Further Mathematics A*, Physics A*, Chemistry A*, Electronics A*.
GCSE: 10 A* (inc Maths and English) + 1 A (French)

Publications

Seelinger, L., Reinarz, A., Lykkegaard, M.B., Akers, R., Alghamdi, A.M.A., Aristoff, D., Bangerth, W., Bénézech, J., Diez, M., Frey, K., Jakeman, J.D., Jørgensen, J.S., Kim, K.-T., Kent, B.M., Martinelli, M., Parno, M., Pellegrini, R., Petra, N., Riis, N.A.B., Rosenfeld, K., Serani, A., Tamellini, L., Villa, U., Dodwell, T.J., Scheichl, R.: Democratizing Uncertainty Quantification. Journal of Computational Physics. 113542 (2024). https://doi.org/10.1016/j.jcp.2024.113542
Kent, B.M., Powell, C.E., Silvester, D.J., Zimoń, M.J.: Efficient Adaptive Stochastic Collocation Strategies for Advection–Diffusion Problems with Uncertain Inputs. Journal of Scientific Computing. 96, 64 (2023). https://doi.org/10.1007/s10915-023-02247-w
In preparation:
Error Estimation through Auxiliary Local Problems for a Stochastic Collocation Approximation of Advection–Diffusion Problems with Uncertain Inputs.

Co-organiser: Manchester Mathematics Research Student Conference online conference, 2020.
Co-organiser: Mathematics of Data Science online student conference, 2020.

Workshop on Frontiers of Uncertainty Quantification, Septmber 2024. Adaptive Stochastic Collocation for Parametric Parabolic PDEs.
The 29th Biennial Numerical Analysis Conference 2023, June 2023. Adaptive in Time Approximation of Parametric Parabolic PDEs.
Manchester SIAM-IMA Student Chapter Conference 2023, April 2023. Adaptive in Time Approximation of Parametric Parabolic PDEs (Best Student Talk Prize Winner). [PDF]
SIAM Conference on Computational Science and Engineering, February 2023. Efficient Adaptive Stochastic Collocation Strategies for Advection-Diffusion Problems with Uncertain Inputs.
SIAM UKIE National Student Chapter Conference, June 2022. Error Estimation for Stochastic Collocation Approximation of Parametric Advection–Diffusion Problems.
IBM Research UK (invited seminar), April 2022. Efficient Approximation of Parametric Parabolic PDEs.
SIAM Conference on Uncertainty Quantification, April 2022. A Posteriori Error Estimation for Stochastic Collocation Applied to Parametric Parabolic PDEs.
26th Annual Meeting of SIAM UKIE Section, January 2022. A Posteriori Error Estimation for Stochastic Collocation Applied to Parametric Parabolic PDEs.

September 2017 – August 2019: Algorithm Developer at Thales, Stockport, UK

Analysis of noisy time series sensor data and array signal processing algorithms.
Analysis of customer experiment datasets and reporting of computational results.
Collaboration with systems engineers to transform customer requirements to algorithm specifications
Collaboration with software engineers to implement algorithm specifications in products.

September 2015 – September 2017: Research Engineer at Thales, Reading, UK

Two-year graduate scheme with training in both technical and core skills.
Four project placements: cryptographic key exchange algorithms, radar signal processing algorithms, filtering and tracking algorithms, array signal processing and data analysis.

July 2014 – August 2014: Rules and Procedures Software Internship, Lloyd’s Register, Southampton, UK

MATLAB: 8 years as primary language (industrial and academic).
Python: FEM approximation via FEniCS and PETSC4py. Interfacing with MATLAB and Julia. Some additional minor industrial projects.
C / C++: Development of cryptographic key-exchange algorithms, undergraduate course, development of FEM software.
Julia: Implementation of novel PDE approximation algorithms. Interfacing with Python.
Experience using UNIX systems.
Version control (git, SVN).
Proficient with LaTeX.