Experience
Research AssociateDepartment of Engineering, University of Cambridge,
Cambridge, UK
September 2025 – Present
- Investigating probabilistic machine learning techniques as part of
the Prob_AI Hub.
- Studied non-linear dynamical systems, and in particular
approximation techniques building upon the linearisation of such systems
via observation (lifting) functions, i.e., the many variants of Dynamic
Mode Decomposition, and machine learning advancements.
- Developed a probabilistic formulation of Koopman Mode Decomposition
with a proper scoring rule based training strategy targeting sharp,
well-calibrated forecasts.
- Implementation using
torch and Hydra for
configuration management, and server-based GPU training.
- Contributed talks to a number of Prob_AI events, and was part of the
organising committee for the Prob_AI Winter School 2027.
Assegnista di Ricerca (Postdoctoral
Researcher)Istituto di Matematica Applicata e
Tecnologie Informatiche “E. Magenes”, Pavia, Italy
February 2024 –
September 2025
- Developed novel multi-fidelity surrogate modelling algorithms for
use with “noisy” solution data, including applications to complex,
coupled modelling problems.
- Focused on data-driven approximation via sparse grid polynomial
interpolation methods and exploiting spectral polynomial approximation
properties. We used adaptive experimental design optimally select
sampling points to fit a multi-fidelity surrogate model. Our novel
algorithm offers robust computationally efficient surrogate modelling
for parametric problems. We focused on applications in uncertainty
quantification and optimisation.
- Implemented and analysed complex test problems including a NASA
turbulent fluid flow validation case via
OpenFOAM and
ParaView, and use of SLURM high performance
computing facilities.
- Numerical experiments required containerisation and deployment of
Python, Julia, and more complex solvers via
Docker and Kubernetes. This included
contributions to benchmark models for the Democratising Uncertainty
Quantification project.
- Contributed to the development of the widely used Sparse Grids
MATLAB Kit and developed a novel
Julia surrogate
modelling package SparseGridsKit.jl.
- Co-organised mini-symposia focusing on surrogate modelling at
Adaptive Modeling and Simulation (ADMOS) 2025 and the Biennial Numerical
Analysis Conference 2025.
- Presented a seminar at Sandia National Laboratories, USA, and an
invited talk for the UM-Bridge Workshop 2024 in addition to a
number of conference talks.
- Supervised by Lorenzo Tamellini, with collaborators at Universitat
Politècnica de Catalunya, Spain.
- IMATI is a Consiglio
Nazionale delle Ricerche (CNR) institute.
PhD in Numerical AnalysisThe University of Manchester, UK
September 2019 –
November 2023
- Industry sponsored ICASE project with IBM Research UK,
supervised by Professor Catherine Powell and Professor David
Silvester.
- Investigated computationally efficient adaptive-in-time
approximation for parametric, time-dependent partial differential
equations.
- Specifically, developed a novel adaptive-in-time sparse-grid
stochastic collocation algorithm for approximation of parametric
time-dependent advection–diffusion problems. Numerical experiments
demonstrated the computational efficiency even for high dimensional
parametric inputs that would be intractable for naive tensor-based
approximations.
- Studied topics in Functional Analysis, Approximation
Theory and Finite Element Analysis, Adaptive Finite Element
Methods, Uncertainty Quantification (Monte Carlo
methods, stochastic collocation, stochastic Galerkin
methods) and Bayesian inverse problems.
- Co-organiser of Manchester Mathematics Research Student Conference
in 2020 and Mathematics of Data Science student conference in 2020.
- Communication of my research included an invited seminar for IBM
Research UK in 2022 and “Best Student Talk” prize at the
Manchester SIAM-IMA Student Chapter Conference 2023.
- Thesis: Efficient Approximation of Parametric Parabolic Partial
Differential Equations.
- Teaching assistant for Matrix Analysis MATH36001, Mathematical
Workshop MATH10001, and Complex Analysis MATH20142.
Algorithm DeveloperThales, Stockport, UK
September 2017 – August
2019
- Developed array signal processing algorithms in
MATLAB
for time-series sensor data. Tested and evaluated algorithms on
large data sets from customer experiments.
- Collaborated with systems engineers to transform customer
requirements to novel algorithm specifications. In particular, worked on
building realistic simulator tools for user training and software
testing.
- Worked alongside software engineers to implement and validate novel
algorithms in delivered product.
- Post event analysis, data cleaning, and reporting for newly
collected experimental data sets.
Research EngineerThales, Reading, UK
September 2015 – September
2017
- Completed a two-year graduate scheme with training in both technical
and soft skills.
- Four project placements:
- implementation of state of the art cryptographic key exchange
algorithms in
C for evaluation,
- fast prototyping of radar signal processing algorithms in
MATLAB and SIMULINK,
- data filtering, multi-target data fusion and tracking algorithms in
MATLAB,
- investigation of environmental effects on array signal processing
algorithms and experimental data analysis.
Mathematics and Physics BSc, First-Class
HonoursThe University of Warwick,
UK
September 2012 – July 2015
- Prize for the best exam results in my cohort.
- Studied modules include Matrix Analysis & Algorithms, Numerical
Analysis & PDEs, Scientific Programming, C Programming.
Rules and Procedures Software
InternshipLloyd’s Register, Southampton,
UK
July 2014 – August 2014
- Upgraded finite element approximation software components from
FORTRAN to C++, in particular algorithms for
element shape validation.
The College of Richard Collyer, Horsham,
UKSeptember 2010 – July 2012
- A Levels: Mathematics A*, Further Mathematics A*, Physics A*,
Chemistry A*, Electronics A*.
- Previously, GCSE: 10 A* (including Maths and English) + 1 A
(French).