About me

I am a second year master student at Stanford University’s Institute for Computational & Mathematical Engineering (ICME). I have a broad interest in applied statistics, probability theory and machine learning theory & application. My origin focuses are on statistical inference and probabilistic modeling, with its applications in with applications in data science, computational neuroscience and quantitative investing. More recently, my work has shifted toward large language models and reinforcement learning, with particular emphasis on post-training research such as function calling, chain-of-thought reasoning, synthetic data and preference optimization. I am currently a Member of Technical Staff at Liquid AI, where I work on post-training research following my Summer 2025 internship and am continuing part-time before transitioning to a full-time role in June 2026.

Before coming to Stanford, I earned my bachelor’s degree in Mathematics from the University of Waterloo, where I majored in Pure Mathematics, Statistics and minored in Computer Science. I’ve been fortunate to work under the guidance of Prof. Liqun Diao and Yi Shen, contributing to projects involving both bayesian statistics and probability theory.

Other than the above interests, I am also open to discuss some graduate level math, which includes real/complex analysis, measure theory, functional analysis and abstract algebras. I haven’t touch them frequently since 2022 due to my focuses changed, but I would like to pick them up since these topic provided me a strong fundation in some theoretical research.

Outside of study, I enjoy swimming 🏊‍♀️, biking 🚴, cooking 👨‍🍳 and traveling 🗺. My MBTI is INTP.

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