About Me

I'm a passionate, detail-oriented data scientist/data analyst with a PhD in Statistics, specializing in statistical modeling, machine learning, data quality, data visualization and data-driven decision-making.

I work with Python, R, SQL, Power BI, Azure and Databricks to transform complex datasets into reliable, actionable insights. My experience spans predictive modeling, spatial and non-spatial statistics, and reproducible analytical workflows.

I enjoy bridging the gap between methods and practical applications, developing solutions that make data easier to understand, analyze, and use.

This portfolio showcases selected projects in data analytics, machine learning, data visualization, and AI-assisted analytics.

Skills

• Languages and Tools: Python, R, TensorFlow, Excel, SQL, power BI, Azure, Databricks, Pandas, Matplotlib, Scikit-learn,
• Areas: Data cleaning, modeling, EDA, spatial and spatio-temporal analysis

Projects

🔗Local Dependence Modeling for Multi-Omics Network Rewiring in Hepatocellular Carcinoma

Detecting nonlinear regulatory changes between protein abundance and phosphorylation using Local Gaussian Correlation

🔗Paired Phosphosite Network Analysis of Tumor-Specific Signaling in Hepatocellular Carcinoma (Azure ML)

Paired Phosphosite Network Analysis of Tumor-Specific Signaling in Hepatocellular Carcinoma (Azure ML)

🔗 DeepKriging for Soil pH Prediction

         *Capturing spatial dependencies for improved agricultural management.*

DeepKriging for Soil pH Prediction

🔗Stability in feature selection

          Improving Reproducibility and Reliability in Gene Expression Classification