I am a Biomedical Engineering researcher at the University of Reading, specialising in functional analysis, scientific machine learning, and brain network dynamics. My work advances the modelling of neural criticality in brain networks and biomarker analysis using fractional function spaces (Sobolev-Slobodeckij and Besov spaces), as well as multiscale physics- and biophysics-informed fractional neural operator learning. I am interested in developing a generalised mathematical framework for describing open, dissipative, and nonequilibrium dynamical systems with long memory and multifractal properties, such as neural criticality observed in brain networks, and in using differentiable and probabilistic programming to develop innovative neural architectures for ground-truth generation and simulation of such phenomena.

As a STEM educator with 16 years of experience, I excel in educational leadership, machine learning applications, and technology-driven curricula across the US, UK, and Caribbean systems. I promote the development of analytical skills through technology-integrated teaching and seek positions in mathematics, physics, or computer science education.

My academic background is heavily mathematical, with a focus on pure and applied mathematics, computer science, theoretical physics, and bioengineering. In particular,

• real and complex analysis; measure theory
• functional analysis and operator theory
• general topology and differentiable manifolds
• ordinary and partial differential equations - neural ODEs
• stochastic processes, stochastic differential equations and neural fSDEs
• fractional calculus, fractional-order differential equations, and fractional neural networks
• machine learning algorithms - neural networks and operator networks
• statistical analysis & distributions - heavy-tail distributions
• numerical and computational methods

PhD, Biomedical Engineering University of Reading, Reading, United Kingdom | on-going (expected 2028)

• Focus: Modelling of neural criticality in brain networks and biomarker analysis using fractional function spaces (Sobolev-Slobodeckij and Besov spaces), as well as multiscale physics- and biophysics-informed fractional neural operator learning
• Core Studies: Functional Analysis (fractional function spaces), Fractional Calculus & Fractional-Order Differential Equations, Stochastic Processes & Stochastic Differential Equations, Time Series Analysis & Forecasting, Statistical Analysis & Heavy-Tail Distributions, Graph & Physics-informed Neural Network Models, Differentiable and Probabilistic Programming.

MSc, Computer Science University of East London, London, United Kingdom | 2023

• Focus: Physics-informed machine learning models for modelling heat transfer diffusion models in solids
• Core Studies: Ordinary & Partial Differential Equations, Generalised 2D Heat Equation, Numerical Methods for PDEs, Artificial Intelligence & Machine Vision , Big Data Analytics & Machine Learning, Advanced Software Engineering, Cloud Computing.

MA, Education University of South Wales, Wales, United Kingdom | 2018

• Focus: Integrating computational thinking, cross-curricular approaches, and real-world authenticity in mathematics education frameworks
• Core Studies: Managing Teaching, Learning and Assessment, Integrating ICT into the Teaching and Learning Processes, Understanding Learning Difficulties and Disabilities, Research Methodology for Education, Education Dissertation

BSc, Mathematics, Computer Science University of the West Indies, St. Andrew, Jamaica | 2008

• Focus: Pure & Applied Mathematics with Applications in Mathematical Modelling and Operations Research; Computational Thinking and Modelling with Applications to Real-World Systems
• Core studies: Real & Complex Analysis, Number Theory, Linear Algebra, Probability & Statistics, Differential Equations, Computer Programming, Software Engineering, Systems Engineering, Database Systems, Network Systems, Intelligent Systems.

Programming & Tools: Python, LaTeX, Markdown, MATLAB, R, C/C++, Git, Bash Scripting, SQL

Scientific Computing: Differentiable and Probabilistic Programming, Scientific Programming & Simulation, Statistical Programming & Data Analysis, Machine Learning Algorithms, Optimisation Techniques.

Research: Scientific/Technical Writing, Literature Reviews, Research Paper Analysis, Poster Design, Curriculum Design