I'm interested in leveraging large scale computational resources for research and development in areas like physics and machine learning. I studied physics in my undergrad at UCSD and was always blown away by numeric and iterative solutions to systems that were otherwise intractable. I discovered that a lot of the same tricks show up in classification, optimization, and predictive modeling approaches in machine learning. In college I always learned best when trying to explain an approach to somebody else. For any topic I want to understand intuitively, I'll do exactly that in the posts on this page. I do my best to build up the algorithms I discuss such that anyone could understand them, though I definitely assume some background in linear algebra.