What I Do

From stunning visualization to hard hitting analysis

The thread which underlies my work is simple: ask hard questions, get concrete answers.

Asking hard questions is essential, and I work collaboratively in all manor of contexts to assure that the questions being asked are the question we want answered. I work to build tools to understand the world, be it tools to mine or collect new data, or tools to visualize and essentialize the relationships data can show us. I work in the middle of the rigor of mathematics and statistics and the complicated landscape where our questions arise from.

I want to build organizational capacity for data science in education and beyond. I aim for an open and transparent data science pipeline. I strive for reproducibility and rigor in analysis. Most importantly, I am always focused on clear communication of goals, methodology, insights, and results.


Who I Am

A Texan with a deep love of mathematics and a passion for education

Raised in the intricately woven and immesurably diverse city of Houston, I grew up in the shadow of the space program and the energy infrastructure which drives our world. My family comes from Bogota, Colombia, where I was born, and is full of educators with a deep passion for serving the most vulnerable populations in the Houston city school districts.

From this context comes my ethos: a dual desire to learn and to apply. An unabashed curiosity about the world and an understanding that mathematics and technology are vital tools to bring to light injustice and inequity.

I attend the University of Texas at Austin, where I study Pure Mathematics in addition to working towards a certification in secondary education. At the seat of the Texas State Government and in an intellectually rich enviornment I have had the privilage of observing the intersection activism, government, and academia in my time in college.


How I Do It

An up to date toolbox is essential: from programming to theory, and everything in between

My toolbox ranges from theory to practice. Below are a few of my choice technical tools for unlocking insight into our world:

  • R - I use R for everything from quick and dirty exploratory analysis, to refined visualizations using Shiny.
  • Javascript, Python - Node.JS is an invaluable data mining tool by virtue of its ability to webscrape so well, as well as a wonderful platform to build out light weight browser based visualizations when nescessary. Python is my main toolset for Machine Learning, including using the wealth of libraries such as Open AI and TensorFlow which are available for it.
  • Statistical Theory- A model or an analyis must be grounded in solid theory. I have taken classes including Theory of Statistics, Applied Statistics, and classes deeper into mathematical theory like Measure Theory, and Riemannian Geometry.
  • Tableau, Stata - These programs are both prominent in different areas where data science is employed, and although they are not my go-to tools, Tableau produces unrivaled fluidity in data visualization, and Stata is a standard when interacting with the social sciences.
  • Research Design - Data Science follows the old adage garbage in, garbage out, so a key component is design of expirments and research to collect data. Between coursework like Research Methods for Science and practical instruction from research with the measurement & evualation focused Data4Peace group, research design is a vital aspect of my skillset.