This repository serves as a centralized, structured knowledge base for my academic research, technical documentation, and professional development. It consolidates learning materials, project logs, and theoretical foundations across several interdisciplinary fields.
The vault is organized into specialized directories reflecting a rigorous approach to continuous learning and research:
- Computational Biology & Bioinformatics: Extensive documentation of the Candida albicans thesis project, alongside comprehensive notes on STOmics, metabolomics (LC-MS), single-cell analysis, and systems biology. Includes coursework from Vrije Universiteit Amsterdam (VU) and the University of Amsterdam (UvA).
- Machine Learning & Artificial Intelligence: Theoretical and applied studies in Deep Learning, Computer Vision, and Bayesian Statistics. This section also tracks emerging trends in AI agents, including specialized courses from Google and Kaggle.
- Cloud Infrastructure & DevOps (AWS): Focused training on Amazon Web Services, covering Cloud Practitioner essentials, Generative AI integration, and DevOps methodology.
- Software Engineering & Development: Technical references and implementation guides for Python, R, and JavaScript. This includes detailed environment configurations for high-performance computing, automation (Selenium), and specialized workflows (ComfyUI).
- Mathematical Foundations: Advanced notes on Linear Algebra, Statistics, and Calculus, providing the necessary theoretical grounding for computational science and machine learning.
The repository is structured to facilitate both rapid reference and deep exploration. Notes are categorized by maturity, from raw research data and meeting logs to polished technical summaries.
To maintain the breadth and depth of this repository, I employ a modern knowledge management workflow:
- LLM-Assisted Synthesis: Large language models are utilized to structure raw notes and summarize complex academic literature according to predefined technical rubrics.
- Expert Review: Every entry, particularly AI-assisted content, undergoes a rigorous manual review to ensure technical accuracy and alignment with established scientific principles.
Note: This repository is a personal knowledge management system. While maintained with professional rigor, it reflects an ongoing research and learning process. All content is for informational and educational purposes.