Mathematics

My education in mathematics thus far has been rather lopsided. I have taken graduate-level real analysis, but have yet to touch abstract algebra (rest assured, I will be taking abstract my senior spring). I am fond of analysis, certainly for of its own beauty, but also because it provides the rigorous underpinnings to my favorite branch of math, probability. Back in high school, when I was taking virtual courses at The Art of Problem Solving, it was the combinatorics and probability course that really spoke to me then. Then, when I participated in the Hampshire College Summer Studies in Mathematics, I took some more advanced probability and had the same feeling. I vividly recall wondering if I could end up pursuing probability in my higher education. Little did I know then that this is exactly what I would do, with probability being the domain of mathematics that I have studied the most. Well, almost the most.

While it feels that my study of probability was destined all those years ago, the biggest surprise for me ended up being my love for statistics. Well, in particular, mathematical statistics. As it turns out, there is much more to statistics than taking t-tests. Who coulda thunk it? It was in Computational Probability and Statistics, learning about the Gibbs sampler, when I was first truly blown away by mathematics. It really felt like probability and statistics could allow you to do magical things, and I knew then that I needed to be the wizard, so to speak. Since then, I have set about creating essentially a toolbox of statistical (and machine learning) methods for myself, with the goal that I could have a nearly endless supply of tools with which to solve any given problem.

Within these posts, you will find some of my thoughts on some of these tools, on other math I find interesting, and other miscellaneous things I deem appropriate to fall under the category of mathematics.

Articles on Math

math - chai harsha