Hi, I'm Binh. Welcome to my small corner of the web.

This Fall, I will begin my PhD in Mathematical Statistics at the University of Maryland, College Park. I was drawn to this path because statistics, to me, is more than a collection of methods: it is a language for thinking carefully in a world that rarely reveals itself all at once. At its best, it teaches us how to reason under uncertainty, how to separate structure from noise, and how to remain intellectually honest about what we know, what we only suspect, and what still lies beyond our reach.

My interests lie broadly in time series, high-dimensional statistics, uncertainty quantification, and machine learning theory. I am especially fascinated by problems where complexity is not a nuisance but the very substance of the question: systems whose signals are faint, noisy, incomplete, or entangled, yet still carry traces of underlying order waiting to be understood.

In my work, I draw on ideas from statistical learning, probability, and geometry to build models with both practical value and rigorous foundations. I am interested in questions that sit at the boundary between theory and application, ranging from probabilistic modeling and sequential data analysis to deep generative methods and mathematical frameworks for complex systems such as those arising in biology.

What continues to move me toward statistics is its peculiar balance of humility and ambition. It asks us to accept randomness not as ignorance alone, but as part of the fabric of reality; and yet, within that uncertainty, it encourages us to search for patterns, regularities, and principles that endure.

I often return to a line by Richard Feynman:

"I learned very early the difference between knowing the name of something and knowing something."

That distinction has stayed with me. Research, for me, is an attempt to move beyond the names of models and the vocabulary of theories toward a more patient understanding of what they mean, why they work, and where they fail. It is often slow and humbling work, but it is precisely that search for clarity beneath complexity that makes it worthwhile.

Here I share some of my projects, notes, and reflections along the way. Thank you for visiting.