I am an undergraduate student studying Joint Honours Mathematics and Computer Science at McGill University. I am broadly interested in statistical machine learning and theoretical computer science, with a focus on building systems that are both mathematically rigorous and computationally robust.
Core Interests
- Uncertainty Quantification: Evaluating the reliability of machine learning systems, specifically through the lens of conformal prediction and distribution-free inference in high-dimensional settings.
- Statistical Inference: Investigating Density Ratio Estimation (DRE) frameworks, kernel methods, and their applications in modeling complex, high-dimensional distributions.
- Optimization: Analyzing the performance and stability of direct vs. indirect estimation methods in practical, real-world scenarios.
Research
I am currently an undergraduate research assistant with Prof. Archer Yang (McGill/Mila). My work centers on the theoretical frameworks for conformal prediction and the optimization of estimation pipelines.
My current research focuses on Density Ratio Estimation (DRE) applied to molecular data. This involves a comparative analysis of methods such as Moment Matching, Probabilistic Classification, and Direct Density-Ratio Fitting (KLIEP, LSIF) to identify why traditional theoretical approaches often fail in high-dimensional structural biology applications. The objective is to develop more stable, optimized estimation frameworks specifically tailored for the complexities of molecular datasets.
Below, you will find a growing digital archive of my technical notes, algorithm implementations, and mathematical derivations.