• Designed and implemented data-driven survey frameworks to transform qualitative feedback into measurable KPIs, enabling stakeholder alignment and showcasing program ROI.• Spearheaded the analysis of engagement, emotional intelligence, and leadership metrics, delivering actionable insights that enhanced productivity, inclusion, and team collaboration.• Developed scalable evaluation models and presented impact-driven results through compelling visualizations, driving strategic organizational improvements.

• Led ML research, developed 10+ major predictive machine learning models including XGBoost and Neural Networks in Python with large NBI datasets under U.S. D.O.T. to predict bridge characteristics better aligned with predictions of current bridge performance in natural hazards, enabling data-driven emergency prioritization planning for WA.• Improved model accuracy by 3-15% through extensive database cleaning and feature engineering leveraging Python and machine learning libraries such as TensorFlow, NumPy, and Scikit-Learn.• Utilized NLP embeddings (BERT and XLNet) to better analyze textual data to improve model performance by 7%.

• Conducted interviews with people involved in the intersection of AI, homelessness, and ethics. • Built machine learning model in R based on predictive factors, using Random Forest and Logistic Regression, that would help shelters best aid people experiencing chronic homelessness.

• Researched how AI and machine learning can be applied ethically in medicine, business, law, and the workplace.

• Implemented linear regression, XGBoost, and other machine learning models to explore different datasets in New York, Texas, and Washington to explore the impact of housing resources on the homelessness crisis.• Developed Shiny App. After the user chooses the rate of people successfully transitioning out of homelessness, my application recommends what type of homeless shelter/program one should use to achieve it.

• Implemented linear regression models to predict cars prices using age, brand, and mileage in Python. • Optimized algorithms with iterative training.

• Researched step count to user health correlation. • Developed and designed mockup of IOS app baseline structure to track user step data anomalies in Python via Apple Health app.