About

A statistician who likes methods, but also likes making them usable.

My work has ranged from feature selection and mixture-model thinking to applied collaboration on electronic health records. Across those settings, I care about making assumptions explicit and workflows durable.

Background

I completed a PhD in Statistics in August 2025 and currently work as a postdoctoral researcher at George Mason University. My training spans methodological statistics, machine learning, and computational work that needs to survive contact with real data.

In practice, that has meant feature-selection research, mixture-model reasoning, stochastic simulation optimization, and applied analysis of electronic health records. I also spend a meaningful amount of time thinking about how technical results are presented, documented, and turned into reusable tools.

What I value

Methods that remain interpretable once they leave an idealized derivation.
Software and analyses that expose assumptions instead of burying them.
Collaboration with domain experts where statistical clarity is part of the deliverable.

Current interests

Right now I am especially interested in Gaussian processes, kernels, model-selection stability, and the design of lightweight interfaces for technical explanation. I follow developments in large language models as a user and builder, but I tend to approach them through a statistical lens.

Links

Open to new collaborations

Looking for work that connects statistical rigor with practical systems.

View CV Email