- Biotechnology undergraduate with a growing focus on applied machine learning, data analysis, and computational workflows.
- Currently strengthening fundamentals in Python-based data science, PyTorch, and reproducible ML workflows
- Applied machine learning on structured and semi-structured data
- Model development, evaluation, and error analysis
- Translating domain problems into quantitative formulations
- Writing clean, version-controlled, reproducible code
- Programming & Systems: Python, R, Docker (basic familiarity)
- Data & ML: NumPy, Pandas, Matplotlib, Seaborn, scikit-learn, PyTorch (learning in progress)
