Analyzed changes in Affordable Connectivity Program (ACP) subscriber rates across U.S. regions, focusing on enrollment declines after the new year due to recertification. Examined demographic factors like race, household size, and device availability, using data mapping and statistical methods to explore relationships between provider coverage (e.g., Xfinity) and subscription rates. Developed hypotheses on broadband infrastructure's impact on enrollment, using multivariate clustering to visualize demographic influences.

Developed a full-stack web app that creates personalized sublease contracts using OpenAI's GPT model. Built an interactive form with HTML, CSS, and JavaScript, connecting a Flask backend via Fetch API. Integrated the OpenAI API for real-time contract generation and implemented CORS for secure cross-origin requests. Demonstrated proficiency in JavaScript, Python, and API integration.

Analyzed asthma risk factors in Oakland and Berkeley neighborhoods through multivariate cluster analysis, focusing on housing conditions, disease prevalence, and demographic factors. Identified areas needing targeted health interventions or housing policy changes, such as anti-asbestos laws or social health programs, to mitigate asthma risks and improve outcomes in vulnerable communities.

Analyzed the impact of COVID-19 on San Francisco's housing market, focusing on home prices, vacancy rates, and rental trends. Used Excel for data analysis, GIS for mapping geographic housing demand, and R for statistical analysis, examining socioeconomic factors affecting migration and vacancies. Leveraged ACS data to assess housing stability and the impact of remote work on preferences, demonstrating proficiency in GIS and spatial data visualization.

Developed the logic of “2048”, implementing features such as tile merging, random tile generation, and scoring calculation. Utilized Java’s List and Deque interfaces to implement and manage tile grids, and movement sequences. Focused on enhancing user interface and implementing player functions for an immersive gaming experience.

Developed a visualization of restaurant review scores using machine learning techniques and the Yelp academic data set. Constructed a  diagram using the Voronoi mathematical algorithm, and  Python, where regions are shaded by the predicted score of the closest restaurant.

Engineered a fully functioning game based on dynamically randomly generating worlds, rooms, hallways through user-input seeds and software engineering. Focused on enhancing user interface and implementing player functions for an immersive gaming experience.

Developed a spam detection binary classifier using logistic regression and advanced text-based features like word count, punctuation, and capital letter frequency. Assessed model performance with accuracy, precision, recall, and ROC curves. Optimized performance using cross-validation and GridSearchCV, achieving over 85% accuracy, showcasing expertise in NLP, classification algorithms, and model tuning.