I'm a Mathematics student at the University of British Columbia interested in statistical modeling, machine learning, and computational methods for analyzing real-world data, with quantitative finance as one of my primary application areas.

• Statistical modeling, inference, and empirical model evaluation
• Machine learning for structured, cross-sectional, and time-series data
• Quantitative research using factor modeling, Spearman Rank IC, quantile analysis, and backtesting
• Reproducible research workflows with Python, Git, automated testing, validation, and reporting

Languages: Python, R, Java, C++, C
Scientific Computing / Data: NumPy, pandas, SciPy, scikit-learn, statsmodels, Matplotlib
Statistics / ML: statistical inference, regression, machine learning, time-series analysis, cross-sectional analysis
Quantitative Research: factor modeling, Rank IC analysis, factor evaluation, multi-factor modeling, backtesting
Tools: Git, GitHub, Jupyter, LaTeX, VS Code

Python-based quantitative research platform for constructing and empirically evaluating cross-sectional equity factors.

• Evaluates factor signals using Spearman Rank IC, ICIR, quantile returns, and long-short diagnostics
• Implements forward-return construction, cumulative-return analysis, Sharpe ratio, and maximum drawdown
• Emphasizes point-in-time data alignment, validation, and reproducible empirical research

Data-driven mathematical modeling of smartphone battery behavior using DXOMARK battery-test data.

• Developed coupled state-of-charge (SOC)–temperature ordinary differential equation models in SciPy
• Calibrated thermal and discharge-efficiency parameters and simulated battery runtime and long-term degradation
• Evaluated model robustness through regression diagnostics and sensitivity analysis, with exploratory analysis of long-term battery degradation

Java-based analytics system for processing, exploring, and visualizing structured datasets.

• Implemented data ingestion, filtering, cleaning, format normalization, and missing-value handling
• Built interactive components for exploring summary statistics, distributions, indicators, and temporal trends

• Applied Statistics
• Statistical Machine Learning
• Computational Statistics
• Quantitative Finance
• Time-Series and Cross-Sectional Data Analysis
• Reproducible Research Systems