🎓 Ph.D. Candidate in Statistics & Data Science
I’m a researcher and data scientist focused on statistics, machine learning, and time series analysis.
I work mainly with climate, hydrological, and high-frequency data, combining statistical methods with practical data workflows.
- Machine Learning & Deep Learning: Building and evaluating predictive models.
- Time Series Analysis: Modeling and forecasting in high-frequency settings.
- Statistical Modeling: Inference, validation, and uncertainty analysis.
- Data Visualization: Clear communication of results for decision-making.
- Applied Data Science: Projects related to climate and water systems.
- Core Languages: Python, R, SQL
- Machine Learning: Scikit-learn, XGBoost, LightGBM
- Deep Learning: TensorFlow, PyTorch, Keras
- Data Analysis: Pandas, NumPy, SciPy, Statsmodels
- Time Series: Prophet, ARIMA
- Visualization: Matplotlib, Plotly, Seaborn
- Data & Workflow Tools: Jupyter, Git, Docker, Linux
- Databases: PostgreSQL, MySQL
- Geospatial: ArcGIS
“Transforming data into actionable insights is not just my profession, it's my passion.”