Featured Projects: Innovation in Action
National Impact: Data Science Leadership for the White House
I played a pivotal role in the U.S. government’s response to the COVID-19 pandemic, leveraging their expertise in data science and bioinformatics to drive large-scale, high-impact initiatives. As the nation’s lead data scientist for the COVID-19 Home Test Kit Mission, Il reported directly to senior staff at the Department of Health and Human Services (HHS) and the White House.
In this capacity, I:
• Developed National Dashboards and Analytics Tools: Created and maintained real-time dashboards to monitor the distribution of test kits across the country. This included integrating heat maps of social vulnerability and equity data, ensuring resources were directed where they were most needed.
• Harnessed Cutting-Edge Technology: Utilized advanced platforms like Palantir Foundry and the Tiberius database to seamlessly link critical data streams, including APIs from the Census Bureau, to provide actionable insights for decision-makers.
• Promoted Equitable Outcomes: Strategically aligned distribution efforts with social vulnerability indices to mitigate disparities in resource allocation, particularly for underserved communities.
• Provided Strategic Leadership: Delivered data-driven recommendations and summaries to top federal leadership, ensuring informed decision-making during a time of national crisis.
Unlocking Insights from Wearable Health Data
This project demonstrates the power of data-driven analysis in revolutionizing personal health management. Leveraging real-world datasets from wearables like Fitbit and Apple Watch, I analyzed activity trends, heart rate metrics, and other health indicators to uncover actionable insights for improving overall well-being.
Key Contributions:
• Data Integration and Preprocessing:
Cleaned and harmonized raw data from multiple sources to create a unified dataset, enabling robust analysis and visualization.
• Advanced Analytics:
Conducted exploratory data analysis (EDA) to identify patterns and correlations between physical activity, sleep quality, and health outcomes.
• Machine Learning Applications:
Developed predictive models to assess how daily habits impact key health indicators, offering insights into optimal routines for improved fitness and wellness.
• Impactful Visualizations:
Created interactive dashboards to present insights in a clear and user-friendly manner, making complex data accessible to diverse audiences.
Why It Matters:
With the growing adoption of wearable health technology, this project underscores the potential of integrating data science with personal health. The analysis provides users with actionable strategies to enhance their well-being and helps researchers explore broader applications in public health and personalized medicine.
Predicting Age Through Multi-Omic Data Analysis
This project explores the fascinating intersection of multi-omics and machine learning to predict human age based on gene expression data. By analyzing a dataset containing expression levels of 20 genes across multiple samples, this project showcases how advanced analytics can unlock biological patterns linked to aging.
Key Contributions:
• Comprehensive Data Exploration:
Conducted detailed exploratory data analysis (EDA) to identify trends, outliers, and key features in the dataset, laying the foundation for robust predictive modeling.
• Dimensionality Reduction Techniques:
Applied Principal Component Analysis (PCA) to distill complex multi-dimensional data into meaningful patterns, enhancing the interpretability of the model’s predictions.
• Predictive Modeling:
Leveraged machine learning techniques, including regression and classification models, to predict individual ages with high accuracy based on gene expression profiles.
• Actionable Insights:
Uncovered key genes most strongly associated with aging, providing valuable insights for researchers investigating age-related biological processes.
Why It Matters:
Understanding the biological markers of aging has profound implications for health, wellness, and medicine. This project highlights the potential of multi-omics data and machine learning to drive personalized interventions and advance our understanding of the aging process.
