• About Me
• Technical Expertise
• Research Areas
• Featured Projects
• Publications and Academic Contributions
• Current Focus
• I'm Always Interested In
• How to Reach Me
• Research Philosophy

I am a passionate researcher and academic focused on developing innovative Machine Learning and Artificial Intelligence solutions to tackle complex engineering problems. My work bridges the gap between theoretical research and practical applications, utilizing advanced computational methods to drive insights and solutions.

• 🔬 Research Focus: Applied ML/AI in Engineering, Computational Modelling, and Data-Driven Solutions
• 💻 Primary Tools: Python, MATLAB, Deep Learning Frameworks
• 🎯 Mission: Transforming engineering challenges into opportunities through intelligent systems
• 📚 Approach: Rigorous academic methodology combined with practical implementation

Engineering Applications

• ⚙️ Predictive Process Control Systems
• 🔧 Optimisation Algorithms
• 📐 Computational Modelling & Optimisation
• 📊 Data Acquisition & Analysis
• 🏗️ System Identification & Parameter Estimation

Machine Learning & AI

• 🧠 Deep Learning (CNNs, RNNs, Transformers, GANs)
• 📊 Statistical Learning & Predictive Modeling
• 🎯 Supervised & Unsupervised Learning
• 🔄 Reinforcement Learning
• 📈 Time Series Analysis & Forecasting

Development Stack

• Python: NumPy, Pandas, Scikit-learn, TensorFlow, PyTorch, Keras, Matplotlib, Seaborn
• MATLAB: Statistical and Machine Learning Toolbox, Deep Learning Toolbox, Optimization Toolbox
• Tools: Jupyter Notebook, MATLAB Livescript, Git, Docker, Linux, HPC Clusters
• Databases: SQL, MongoDB
• Cloud: AWS, Google Cloud Platform

Note: My research projects are in private repositories or under review for publication. Feel free to reach out for collaborations or inquiries about specific work.

• 📄 Research papers and conference presentations
• 🎓 Contributions to academic community
• 🏆 Awards and recognitions
• 👥 Collaborative research projects

Publications list available upon request or see my academic profile

• 📄 Mathematical modelling and optimisation of fouling problems in membrane bioreactor
• 🎓 Hybrid ML models combining mechanistic physics-based and data-driven approaches
• 🏆 Applied research in Machine Learning for Engineering applications
• 👥 Open-source Machine Learning tools and frameworks in Engineering applications

• 📄 Research Collaboration: Interdisciplinary projects combining ML/AI with engineering simulations
• 🎓 Consulting: Applying ML/AI and numerical simulations to solve practical engineering problems
• 👨‍🏫 Mentoring: Guiding students and early researchers in ML/AI and engineering simulations
• 🏆 Knowledge Sharing: Discussions on latest trends in ML/AI and engineering applications

"The best way to predict the future is to invent it, and the best way to invent it is through rigorous research, innovative thinking, and practical application of machine learning and artificial intelligence to solve real-world problems."