🎓 Academic Background
Currently pursuing studies at the University of St Andrews in Scotland, with a focus on:
- Bayesian Statistics - Probabilistic modeling and inference
- Cluster Analysis - Pattern recognition in complex datasets
- Machine Learning Applications - Bridging theory and real-world problems
🔬 Research Interests
My work spans several interconnected areas:
Computational Biology - Deep learning applications in bioinformatics, particularly virus variant detection and genomic enhancer analysis
Human Motion Analysis - Machine learning approaches to classify and predict movement patterns using sensor data
Urban Analytics - Big data analysis of transportation systems and behavioral patterns
Statistical Computing - Developing efficient algorithms for complex data analysis
🌟 Research Philosophy
I believe in making complex statistical concepts accessible while maintaining mathematical rigor. My approach combines:
- Theoretical Foundation - Strong mathematical and statistical background
- Practical Application - Real-world problem solving
- Open Science - Sharing knowledge and reproducible research
- Interdisciplinary Collaboration - Working across domains
💡 Current Focus
Working on projects that integrate machine learning with domain expertise, particularly in biological and social sciences. I’m passionate about developing interpretable models that provide both predictive power and scientific insight.
📚 Beyond Research
When not immersed in data analysis, I enjoy exploring the beautiful Scottish countryside, experimenting with coffee brewing methods, and contemplating the perpetual question of whether it’s raining or just misting outside.
📬 Let’s Connect
Always interested in discussing research, collaboration opportunities, or simply sharing thoughts about statistics and data science over a good cup of coffee.
“The best way to learn is to teach, and the best way to understand is to explain.”