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

Experience

February 2024 – Present: Assegnista di Ricerca (Postdoctoral Researcher) at Istituto di Matematica Applicata e Tecnologie Informatiche “E. Magenes” (IMATI), Pavia, Italy

• Supervised by Lorenzo Tamellini. IMATI is a Consiglio Nazionale delle Ricerche (CNR) institute.
• Development of novel multi-fidelity surrogate modelling algorithm for use with “noisy” solvers.
• Focus on non-intrusive approximation via sparse grid methods and exploiting spectral polynomial approximation properties.
• Development of test cases including parametric elliptic and parabolic PDEs and using benchmark Reynolds-Averaged Navier-Stokes simulations.
• Containerisation and deployment of research models via Docker and Kubernetes.

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

• Supervised by 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.
• Focused on a combination of finite element method, adaptive timestepping and sparse polynomial approximation.
• Developed hierarchical and residual based error estimation strategies in the context of novel adaptive approximation algorithms.
• Also studied topics in Functional Analysis, Approximation Theory and Finite Element Analysis, Adaptive Finite Element Methods, Uncertainty Quantification (Monte Carlo, stochastic collocation, stochastic Galerkin methods) and Bayesian inverse problems.

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

• Development of array signal processing algorithm for time series sensor data.
• Analysis, evaluation and reporting for customer experiments.
• 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, data fusion and tracking algorithms, array signal processing and experimental data analysis.

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

• Prize for the best exam results in my cohort.

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

• Upgrading FEM software components from FORTRAN to C++.

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
• Kent, B.M. Efficient Approximation of Parametric Parabolic Partial Differential Equations. PhD Thesis, University of Manchester (2024). [eThesis at Univeristy of Manchester]

In Preparation

• Kent, B. M., Tamellini, L., Giacomini, M., Huerta, A.: Multi-Fidelity Surrogate Modelling for “Noisy” Solvers via a Novel Multi-Index Stochastic Collocation Algorithm.

Conference Organisation

• Co-organiser: Minisymposium on Adaptive sampling and surrogate/reduced order modelling strategies for parametric differential equations, XII International Conference on Adaptive Modeling and Simulation (ADMOS), June 2025.
• Co-organiser: Manchester Mathematics Research Student Conference online conference, 2020.
• Co-organiser: Mathematics of Data Science online student conference, 2020.

Conference Talks

• UM-Bridge Workshop 2024, December 2024. An UM-Bridge-based setup for multi-fidelity surrogate models for UQ (invited talk). [PDF] [YouTube]
• CNR-IMATI Internal Conference, November 2024. Multi-fidelity Approach for Uncertainty Quantification of a Fluid Dynamics NASA Test Case.
• Workshop on Frontiers of Uncertainty Quantification, September 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, April 2022. Efficient Approximation of Parametric Parabolic PDEs (invited seminar).
• 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.

Programming Experience

• MATLAB: Industrial algorithm development and data analysis. Research tool for investigations into the approximation of parametric partial differential equations.
• Python: FEM approximation via FEniCS and petsc4py, design of model interfaces for containerisation.
• Julia: Implementation of novel PDE approximation algorithms and development of sparse grids approximation package SparseGridsKit.jl. Interfacing of Julia code with existing Python packages.
• OpenFOAM and ParaView: Experience as a user for Reynolds-Averaged Navier-Stokes turbulence modelling including mesh generation, solver configuration, post processing and analysis.
• Docker and Kubernetes: Containerisation of software models via Docker and deployment via Kubernetes. Also have experience with SLURM systems.
• C / C++: Implementation of cryptographic key-exchange algorithms, development of industrial FEM software.
• Windows systems, Unix systems, version control systems and workflow automation, LaTeX.

Teaching Experience

• University of Manchester: Teaching assistant for Matrix Analysis MATH36001 (semester one, 2021), Mathematical Workshop (MATH10001, semester one, 2021), for Complex Analysis MATH20142 (semester two, 2020).