I build machine-learning pipelines, LLM applications, and analytical reporting tools using Python and SQL. My work spans EEG classification, retrieval evaluation, CRM automation, and regulatory-data analysis.
Currently, I’m an AI R&D Intern at the University of Memphis, working on EEG seizure classification, patient-aware evaluation, and reproducible experiments. I hold an M.S. in Data Science.
| Area | Technologies |
|---|---|
| Programming & ML | Python, SQL, R, scikit-learn, XGBoost, LightGBM, pandas, NumPy, SciPy |
| Data analysis & BI | Power BI, Sigma, Tableau, Microsoft Excel, Streamlit, Matplotlib, Seaborn |
| Data engineering | Apache Spark, PySpark, DuckDB, PostgreSQL, MongoDB, MySQL |
| Deep learning & biomedical AI | PyTorch, TensorFlow, Keras, VGG16, Swin Transformer, MNE-Python, YASA |
| Generative AI & RAG | LangChain, LangGraph, LangSmith, RAGAS, Sentence-Transformers, Pydantic, OpenAI SDK, Ollama |
| Vector search & retrieval | FAISS, Annoy, ChromaDB, PostgreSQL/pgvector, OpenSearch |
| Backend & APIs | FastAPI, SQLAlchemy, Redis, Celery, httpx |
| MLOps & cloud | Docker, Docker Compose, DVC, MLflow, GitHub Actions, AWS EC2, AWS ECR, Amazon S3, Amazon CloudWatch, Google Cloud Platform |
- Define dataset checks, baselines, and evaluation splits before comparing models.
- Test APIs and integrations, including duplicate requests, partial failures, and retry behavior.
- Document reproducible commands, benchmark conditions, and limitations alongside results.
