Applied Mathematician & Scientific Machine Learning Researcher
I develop numerical methods for PDEs, fluid dynamics, and inverse problems, with a focus on reduced-order modelling and scientific machine learning. My work combines mathematical analysis, reproducible simulation, and experience building machine-learning and data systems in industry.
Open to PhD, research, and scientific ML engineering opportunities.
Applied ML & engineering → Download CV ↓
Selected work
All projects →
Research figure · open case study for context
Scientific ML · Model reduction
Reduced models & neural operators
Research implementation
Comparing fast approximations of nonlinear transport.
ROM error: 0.152% in-distribution; 1.7% on the tested unseen initial-condition family.
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Research figure · open case study for context
Dynamical systems · Uncertainty
Data assimilation for chaotic Lorenz-96
Research implementation
Estimating chaotic system states from noisy observations.
Localisation reduces RMSE from 4.63 (a diverged unlocalised filter) to 0.41, below the observation noise (10 seeds).
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Research figure · open case study for context
M.Sc. thesis · Applied mathematics
Optimal mixing of passive scalars
2021 thesis · resolution study extended in 2026
Reproducible pseudo-spectral study of optimal passive-scalar mixing.
Extended to N=512: the apparent resolution dependence of the fitted mixing exponent is primarily a moving-fit-window effect; on fixed time windows it converges at about second order.
Read case study →Simulation, imaging & learning
Explore the work →Figures from my computational studies. Open a project for its methods, evaluation settings, and limitations.
Research themes
Scientific machine learning
Reduced models and learned surrogates, with attention to computational cost and generalisation beyond training data.
Inference & uncertainty
PDE-constrained inversion, Bayesian inference, optimisation, and ensemble data assimilation.
Numerical simulation
Spectral and finite-element methods for flow, transport, and mechanics, checked through convergence studies and reference solutions.
From mathematical models to working systems
Aviation analytics architecture · explore the case study →My industry experience spans aviation analytics, predictive modelling, and engineering software. At Intuos Srl, I developed a dashboard serving flight-phase classifiers over recorded telemetry. Its classifier achieved 0.999 weighted F1 in five-fold row-level cross-validation, measured against labels produced by the existing PositionAssigner model, not independently verified flight phases.
Background
I hold an M.Sc. in Mathematical Engineering from the University of L’Aquila (2021) and a B.Sc. in Mathematics from Obafemi Awolowo University (2018). My Master’s thesis investigated mixing-rate bounds for passive scalars in incompressible flow.
Let’s discuss research & collaboration
I am based in L’Aquila, Italy. Get in touch about research, scientific computing, or applied machine learning.
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