Introductions are tricky. Where are you supposed to begin? I think I’ve had a pretty interesting life so far, of course, I am biased, but I’m not going to go deep into any of that, right now.

As an undergraduate I had the opportunity to work on many interesting projects, most of which were computational, and can be found on my github with poor to nonexistent documentation.

I thought about cleaning up my past work and presenting it here, but while that might be interesting to readers (if there are any), it sounds boring to me.

I need to move on to the next thing that drives my curiosity. There is so much more to learn and I have to do myself a favor and focus on what’s ahead.

Roadmap

As mentioned on the about page, I have become very interested in quantum field theory, which is a fascinating subject that combines many seemingly unrelated aspects of physics, mathematics, and computer science. Of course, the same could be said of quantum information science, which recently got bumped from the top spot of my research interests. Even still, QFT and QIS are connected, in some very interesting ways that I hope to explore in the future.

While theoretical physics is my passion, I also need to pay rent and fund my culinary explorations. To that aim, I also plan on spending a substantial amount of time researching and practicing methods in data science, specifically those related to machine learning and statistical analysis. I do actually find these fields tantalizing as well.

Below is an outline of topics I hope to explore on this blog:

physics

  • quantum field theory
  • gauge theory
  • general relativity
  • quantum information theory
  • statistical physics
  • condensed matter theory
  • string theory

mathematics

  • differential geometry
  • smooth manifolds
  • topological field theory
  • stochastic differential equations
  • calculus of variations
  • Lie groups & Lie algebras
  • representation theory

computational & data science

  • machine learning
    • neural networks
    • kernel methods
    • reinforcement learning
  • topological data analysis
  • statistical inference
  • numerical optimization
  • quantum algorithms
  • data visualization