I am studying the Data Science Engineering Major in Mexico. The program consists of a mix of the basics in:

• Computer Science,
• Software Development,
• Data Engineering,
• Statistical Learning.

I intend to write a thesis on the latter, focusing on kernel methods.

I spent most of my college life in the Physical and Mathematical Sciences Department of my university. Health didn't permit me take courses every year, so my absence ruled out graduating with a Major in Mathematics as I intended despite completing a full 3 years of courses.

I turned that setback into a strength by leaning into the Computer Science Department. You can hardly put it as pivoting, it's a continuation to my education that goes well with my mathematical background.

I am more oriented towards writing reports and documenting analytical problems, those that have mathematical models. My code in those projects are often ad-hoc due the time restraints, but I do not waste the opportunity to add a new concept I wanted to try.

For instance Python with its OOP and first class functions is a good playground, even though I am more fond of (still high level) languages that hide less below the surface, like C++.

I am particularly fond of anything that deals with errors, estimation and approximation: wavelets and spaces of functions, and the ideas of functional analysis. My way of going through books nowadays is more "exploratory" than sitting strictly through the classical "definition-theorem-proof" loop.

Why I like it? Because I like having the formal definitions burned into my mind, they give you a "target" to identify when a complex problem arises. If not for the theorems and applications, only the language you get from Mathematics is worth it.

• Literature: This entry wouldn't be here without Dostoevsky who got me into buying a Kindle.
• Simracing and FPS multiplayers: sadly, both with a controller.
• Music: aggressive and melodical music, but also OSTs and a lot of Pop/Indie.