I am a Glasgow-based 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 LSTMs to solve complex energy challenges.

• 👨‍💻 Role: Data Scientist, Machine Learning Engineer, AI Researcher.
• 🧠 Top Skills: 🐍 Python, TensorFlow (Deep Learning), 📉 Time-Series Forecasting, Mathematical Modelling.
• 🚀 Business Value: I don't just build models; I use Physics-Informed ML to optimize systems, reduce operational costs, and drive decarbonization.
• 🔭 Currently working on: Integrating Deep Learning with Subsurface Thermal Energy Storage (STEaM) simulations to predict long-term thermal behavior.
• ⚡ Core Expertise: Renewable Energy Systems, Thermodynamics, Systems Modelling, and Predictive Analytics.
• 🤝 Looking to collaborate on: AI-driven energy decarbonization projects and predictive maintenance models.
• 🇬🇧 Status: UK Global Talent Visa Holder (I can work for any employer immediately without sponsorship).

Category	Stack
Languages
Machine Learning
Data Processing
Visualization
Dev Tools
Cloud Platforms
Databases
Research Domain	Time-Series Forecasting Thermodynamics Renewable Energy Deep Learning Optimization

Industry Application: Predictive Maintenance & Energy Grid Optimization

• The Challenge: Predicting heat retention in subsurface storage was too slow using traditional physics engines.
• The Solution: Developed a TensorFlow LSTM (Recurrent Neural Network) to learn from historical sensor data.
• The Impact: Reduced simulation runtime by 90%, enabling real-time decision-making for energy storage.
• Stack: Python TensorFlow Keras Pandas Google Colab

Industry Application: System Efficiency Improvement

• The Challenge: Solar collectors were underperforming due to static configuration parameters.
• The Solution: Wrote custom Genetic Algorithms (Optimization) to cycle through thousands of design variables.
• The Impact: Identified a configuration that increased energy capture by 30%.
• Stack: MATLAB Optimization Data Visualization

Industry Application: Automated Valuation & Pricing Engines

• The Challenge: Traditional linear models failed to capture complex non-linear interactions between categorical attributes (cut, clarity) and price for accurate valuation.
• The Solution: Engineered a custom ResNet-MLP (Deep Learning) architecture using TensorFlow/Keras, implementing residual skip connections and Log-Norm target engineering to stabilize gradients.
• The Impact: Delivered a production-ready pipeline capable of real-time price inference, targeting an accuracy of R² > 0.95.
• Stack: TensorFlow Keras Pandas Scikit-Learn ResNet

Industry Application: Healthcare Analytics & Resource Planning

• The Challenge: The Scottish Government needed rapid projections of ICU bed usage.
• The Solution: Applied statistical modelling to patient intake data to forecast demand spikes.
• The Impact: Directly supported public health resource planning during a critical crisis.
• Stack: Python Scikit-Learn Data Analysis