Based in Glasgow, UK 🇬🇧

I am a Ph.D. Researcher and Data Scientist bridging the gap between theoretical physics and commercial AI applications. My work focuses on Physics-Informed Machine Learning, utilizing TensorFlow and Python to solve complex challenges in Energy Systems and Predictive Analytics.

• 💼 Visa Status: UK Global Talent Visa Holder (Eligible to work immediately, no sponsorship required).
• 🔭 Current Focus: Deep Learning for Time-Series Forecasting & Techno-Economic Modelling.
• 🚀 Core Competencies: Finite Volume Simulation, Genetic Algorithms, and production-grade ML pipelines.

Domain	Tools & Frameworks
Languages
Deep Learning
Data Science
Visualization	Matplotlib Seaborn Power BI Data Storytelling

Physics-Informed Simulation Engine

• The Challenge: Modeling thermodynamic stratification in legacy mine shafts to validate GigaWatt-hour thermal storage.
• The Engineering: Built a 1D finite volume simulation engine in Python from scratch.
• The Impact: Generated critical LCOH and COP metrics, de-risking the conversion of industrial liabilities into renewable assets.
• Tech: Python NumPy SciPy Finite Volume Method

High-Precision Asset Valuation

• The Architecture: Custom ResNet-MLP architecture using TensorFlow/Keras with residual skip connections and Log-Norm target engineering.
• The Result: Achieved R² > 0.95, creating a scalable solution for complex non-linear pricing inference.
• Tech: TensorFlow Keras Deep Learning Feature Engineering

Public Sector Healthcare Analytics

• The Scope: Analyzed official NHS Scotland open data to model ICU bed usage during critical demand spikes..
• The Impact: Developed a statistical framework for patient intake projection, demonstrating how data-driven insights support public health resource allocation.
• Tech: Python Scikit-Learn Predictive Modelling

Get in touch via LinkedIn to discuss Data Science or Quantitative Analysis opportunities in Glasgow.