I'm a Mathematics student at the University of British Columbia interested in statistical modeling, computational biology, and reproducible machine learning. I use data and quantitative methods to study biological systems and financial markets.

• Statistical modeling, inference, and rigorous model evaluation
• Computational biology and biomedical data analysis
• Machine learning for structured, image, and time-series data
• Reproducible research workflows with Python, Git, testing, and documented validation

Languages: Python, R, Java, C++, C
Scientific Computing / Data: NumPy, pandas, SciPy, Polars, Zarr, scikit-learn, statsmodels
Machine Learning: PyTorch, statistical learning, regression, cross-validation, model evaluation
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.

Research pipeline for detecting cells in 3D microscopy and reconstructing their lineages over time for the Biohub Kaggle competition.

• Adapted the official detection and association baseline and built reproducible training, inference, and evaluation workflows.
• Developed graph-based lineage reconstruction with division filtering, gap recovery, and geometry checks that respect physical voxel spacing.
• Validated against the source-locked official metric; the reviewed submission received a 0.823 public leaderboard score. This is a public score, not a final private score or rank. Result record · Submitted notebook version

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.

• Biostatistics and Computational Biology
• Applied and Computational Statistics
• Statistical Machine Learning
• Quantitative Finance
• Reproducible Research