- 🔬 Data Engineer deeply passionate about data's N dimensionality
- 🤖 Knowledgeable in Machine Learning and Data Mining
- 💹 Currently researching Large Language Models (LLMs) in Finance
- 🧬 Background in protein structure prediction and bioinformatics
- 🔗 ORCID | LinkedIn
Languages & Notebooks
ML / Deep Learning
Data & Computation
Databases & Vector Stores
Streaming & Infrastructure
| Project | Description | Tech |
|---|---|---|
| Fraud-Detection-Framework | Agentic RAG pipeline with Neo4j graph analytics, ChromaDB RAG, and real-time Kafka streaming on 590K transactions | LightGBM, Neo4j, ChromaDB, Kafka, Docker |
| Large-Language-Models-from-Scratch | Building LLMs from the ground up — BPE tokenization, encoder-decoder architectures, and next-token prediction | Python, Jupyter, Docker |
| Generative-AI-Concepts | Foundations of Generative AI — Attention Mechanisms, LSTMs, GANs, and Neural Machine Translation | TensorFlow, Keras, NumPy |
| Deepcon_Precision | Protein contact prediction using precision matrix features (441 channels, 3456 proteins) | Python, Deep Learning |
| Feature-Importance | Systematic feature blurring to rank importance across 14 features | Python, ML |
| Feature-Extraction | Feature extraction pipeline for structured data | Python |
| Noise-Matrix | Noise matrix generation and analysis | Python |
⭐ If you find my work useful, feel free to star the repositories!