Hello World

Hello world,

I study problems in theoretical computer science and large-scale data systems with applications to machine learning. My current work sits at the intersection of statistical learning theory and machine learning systems.

My intuition is that theoretical models can extend to empirical results and that informative answers come from abstracting experimental conclusions into concrete theorems and proofs. The problems I find the most interesting connect simple theoretic ideas to practical applications.

As proofs and experiments become much cheaper to produce, the qualifying bar for useful/interesting research increases substantially. Additionally, the interpretation of the research results becomes much more valuable. With this in mind, these are some of the problems that I find interesting: which patterns emerge in large-scale systems? Can these scaling behaviors be abstracted into predictable properties? Which properties of the data and model determine the efficiency and efficacy of the system?

One of the many joys of computer science research is the compatibility of theory and experimentation. Theoretical ideas can be cheaply tested (relatively speaking) on widely accessible hardware without bureaucratic processes. This solves an important problem: too many theoretical ideas yield zero practical applications, and many experiments produce unexplainable results. The ability to condense asymmetric amounts of information and connect these observations to mathematical foundations provides computer scientists with a unique and powerful ability!

Candidly, I expect some of my conjectures to be incorrect and revised. These posts are merely ideas that I am still trying to formalize and build into deeper theories. The overall goal is to make the research available to the wider scientific community. Nonetheless, I will attempt to make these ideas as well developed as possible (and limit my reliance on LLMs).

These notes are written for other researchers interested in TCS and large-scale systems, though I hope they remain accessible to anyone curious about learning theory and why machine learning behavior. If something here is useful (or wrong), I would be glad to hear about it and feel free to reach out.

Thanks for reading!