Applied Statistics & Mathematics at the University of Toronto.
Researching at the intersection of
Statistics is the language through which uncertainty becomes knowledge.
I'm a student at the University of Toronto pursuing an Applied Statistics Specialist and Applied Mathematics Major. My work sits at the boundary between rigorous statistical theory and practical machine learning, with a particular focus on Natural Language Processing.
I'm drawn to problems where mathematical structure meets real-world complexity — building models that are both theoretically grounded and genuinely useful. From Bayesian inference and probabilistic modeling to deep learning architectures, I enjoy the full spectrum from theory to implementation.
Outside of research, I care deeply about clean, reproducible code and the kind of collaborative science that moves the field forward.
"The purpose of computing is insight, not numbers."
— Richard Hamming
Beyond the mathematics and code, I'm someone who finds joy in understanding how things work at a deep level — whether that's a statistical model, a piece of music, or a philosophical argument. I believe the best researchers are those who stay genuinely curious about the world.
I'm particularly interested in the philosophy of statistics — questions about what probability means, how we should reason under uncertainty, and the epistemology of scientific inference. These aren't just academic questions; they shape how I approach every analysis.
Music has been a central part of my life since I was four and a half, when I first picked up the cello. I spent four years as a member of the symphony orchestra at the High School Attached to Northeast Normal University, which gave me a deep love for ensemble playing and classical repertoire that has stayed with me ever since.
Outside of academia and music, I enjoy playing badminton, exploring new ideas across disciplines, and the occasional late-night debugging session that somehow becomes a learning adventure.
"All models are wrong, but some are useful."
Always open to research collaborations, internship opportunities, or simply an interesting conversation.