UCLA M.S. Statistics: The Mathematical Foundation of Data Science

My graduate studies in Statistics at UCLA provided the rigorous mathematical foundation that enables me to build robust, reliable data systems and analytics solutions. Below you'll find a comprehensive collection of my coursework, projects, and thesis work that demonstrates the statistical thinking and technical skills I developed during my time at UCLA.

Through courses in advanced statistical methods, modeling, computing, and time series analysis, I developed expertise in R programming, statistical modeling, experimental design, and data visualization. My coursework culminated in a comprehensive thesis project that showcased advanced statistical analysis, machine learning techniques, and to link causal relationshps between LAUSD funding programs and student outcomes.

Master's Thesis

Masters Thesis on Proquest

Thesis Github Code - Complete codebase for my UCLA MASDS (Master of Applied Statistics and Data Science) thesis project, showcasing advanced R programming and statistical analysis capabilities. GitHub repository.

Academic Work

Feed-Forward Panel Estimation for Discrete-time Survival Analysis of Recurrent Events with Frailty - This research tackles a complex problem in survival analysis: predicting when events will happen multiple times for the same person or system. Traditional methods struggle because they can't easily account for hidden differences between subjects that affect their risk. Our solution, called FFPSurv, uses advanced statistical techniques to:

• Track individual risk patterns as events occur over time
• Update predictions as new data becomes available
• Provide mathematical guarantees that the model can be properly estimated We're testing this approach on both simulated data and real-world examples, proving it works better than existing methods. The mathematical framework ensures our results are reliable and reproducible.

Lost in the Stars - Advanced statistical methods to find objects in photos from the James Webb Telescope, demonstrating proficiency in R programming and statistical modeling.

Classifying Players by Position and Performance - Final project presentation highlighting advanced statistical analysis techniques and R programming capabilities in data science applications.

Discrete Markov Chain Model for Analyzing Probability Measures of P2P Streaming Network - Final project presentation showcasing advanced statistical methods and R programming expertise in comprehensive data analysis.

Exploring Meta Trends with Multivariate Statistical Analysis - Comprehensive statistical analysis and R programming capabilities through a complete data science workflow, including GitHub repository.

Fish Sale Seasonal Forecasting - Comprehensive time series analysis project using R to forecast fish sales with seasonal patterns, demonstrating advanced statistical modeling and forecasting techniques. Includes GitHub repository.

Multivariate Methedologies: Google Ad Cohort Performance Analysis - Final project presentation demonstrating advanced statistical modeling techniques and R programming capabilities in data analysis.