I’m a theoretical physicist with several years of research experience. I have worked on several string theory topics, from the old on-shell strings to the modern holographic correspondence between gravity and quantum field theory. However, my expertise is the statistical modeling of quantum black holes, more precisely on the connection between black hole entropy and number theory.

These days I have been interested in machine learning and data science. Like many people in the field, I got hooked by recent developments in deep learning. Physicists are usually interested in running experiments and exploring what the universe outputs. Intriguingly, running a machine learning program on a computer is a very similar experience. In physics, we poke the universe and see what comes out of it, but with a computer that can fit in your lap, one can access multiple universes of information. A fascinating paradox!

My goal is to bring insights from physics and applied them in this field. But first, let’s start with the fundamentals and basic building blocks of machine learning and data science.