I am a Ph.D. candidate at UT Austin, working in statistics and machine learning (ML) for scientific applications supervised by Professors James Scott and Cory Zigler. I collaborate with researchers from diverse scientific domains, such as cancer, air pollution, robotics, epidemiology, and transportation. I approach problems using a combined Statistics+ML mindset (to exploit structure-rich data while understanding the data-generating process and sources of confounding). Within Statistics, I specialize in large-scale inference for spatio-temporal data, Bayesian modeling, and causal inference. In ML, I’ve worked on deep learning applications, particularly for images and reinforcement learning (RL). Lately, I have been investigating sequential decision-making (including RL) for adaptive policies, treatments, and experiment design in health care and public policy problems.
At the beginning of the Covid-19 pandemic, I joined the UT Covid-19 Modeling Consortium, where I helped building forecasting models to advise the CDC. Recently, I became part of the Learning Agents Research Group (LARG) and the Robocup UT Austin Villa Team team led by Prof. Peter Stone. Working with robots is a lot of fun, and it has shaped my understanding and ability to tackle problems in other applied scientific domains using cutting-edge AI tools. Previously, I did internships at Intel AI and Facebook AI Research.
Outside research, I play tennis and paddleboard on Lady Bird Lake with my wife, Michelle. I also like learning languages. I am currently studying Japanese.
PhD in Statistics, 2017 — 2022
University of Texas at Austin, USA
MASt in Mathematics, 2014 — 2015
University of Cambridge, UK
BSc in Applied Mathematics, 2007 — 2012
Mexico Autonomous Institute of Technology (ITAM), MX