El Segundo, California, United States
M.S. in Applied Data Science and B.S. in Applied & Computational Mathematics from the University of Southern California. My background spans statistical modeling, machine learning, and data engineering with a focus on translating complex data into actionable insights. As a Teaching Assistant for USC’s Applications of Machine Learning course, I helped students develop intuition for real-world ML workflows in Python using NumPy, pandas, and scikit-learn. This greatly strengthened my ability to communicate technical concepts clearly and effectively. Previously at UCLA Health, I designed a dynamic cost observability dashboard in Python and SQL, implemented 90-day forecasting models, and optimized data architecture for scalable analytics. I’ve also contributed to AI training initiatives at Outlier AI, evaluating and refining large language model reasoning for math-focused tasks. I’m passionate about building systems that connect data, intelligence, and impact. My skillset combines technical rigor with a mindset for continuous learning and collaboration.
- Built end-to-end ML pipeline with PostgreSQL feature extraction, cross-validated Random Forest training, Platt probability calibration, and grid search hyperparameter tuning - Designed feature engineering system using modular extractors, mutual information selection, leakage detection, and stratified train/test splitting - Developed production inference layer with real-time prediction serving, quantile-based risk stratification, and fuzzy matching for multi-source data integration
- Facilitate learning for 50+ students by holding weekly office hours (2+ hours) to guide students through solving Python coding problems in data manipulation and machine learning using libraries like NumPy, pandas, and scikit-learn. - Enhance student understanding by actively managing the course question board, providing timely responses to machine learning queries, including data preprocessing and model evaluation. - Elevate student performance by grading assignments and projects with constructive feedback, emphasizing optimization of machine learning models and Python code quality.
- Developed a dynamic dashboard using Python and SQL to analyze and visualize Databricks workflow costs, delivering actionable insights across multiple departments and streamlining cost reporting processes. - Implemented a highly reliable machine learning model for cost forecasting to predict workflow expenses over a 90-day period, enabling proactive budgeting and improved financial decision-making. - Optimized data management and analytics workflows by contributing to scalable data architecture frameworks, reducing data processing time and supporting advanced analytics and machine learning projects.
- Led hundreds of training sessions for diverse student groups, tailoring instruction to various skill levels while fostering a disciplined, supportive environment that promoted self-confidence, resilience, and skill development. - Devised and implemented structured training routines, coordinating with fellow instructors to ensure consistency, effectiveness, and alignment with students’ developmental goals. - Took initiative to mentor junior instructors, providing guidance on teaching techniques and leadership skills, ensuring a cohesive team dynamic and consistent quality of instruction across all classes.
- Enhanced the training of Large Language Models (LLMs) by crafting and answering complex math-related questions, improving the models’ ability to handle advanced problem solving-tasks. - Optimized model performance through systematic evaluation and ranking of LLM-generated responses, providing actionable feedback for refining response accuracy and quality. - Improved the accessibility and practical application of AI models by ensuring their outputs aligned with real-world mathematical standards, enabling their integration into educational and professional tools.