Hello World

Hello world,

I study problems in theoretical computer science and large-scale data systems with applications to modern machine learning, representation, and compression. 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 incorporate both theory and applications: what patterns emerge in large-scale systems? Can these scaling behaviors be abstracted into predictable properties? Which of the data and model's parameters 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 ability: to simplify enormous complexity.

This blog was inspired by the extraordinary academic contributions of professors, researchers, and podcasters. I aspire to impart even a fraction of scientific contribution as these giants of machine learning research.

This site is meant to be an in-progress notebook. 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 organize 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 the use of LLMs in my writing).

These notes are written for other researchers interested in TCS and large-scale systems, though I hope they remain accessible to anyone curious about theoretical learning and why machine learning systems behave the way they do. If something here is useful (or wrong), I would be glad to hear about it. Progress on these questions is not an individual undertaking; we all stand on the shoulders of giants.

Thanks for reading.