Bio: I took a rather non-linear path to get where I am. Growing up I always wanted to be an aerospace engineer, or at least since I found out my eyesight was too bad to be a pilot. I abandoned this dream when my high school physics teacher told me I was terrible at math. She might have been dead wrong, but believed her; so in lieu of engineering, I decided to attend Virginia Commonwealth University to pursue my other passion: guitar.

Thankfully I was able to realize that earning a degree in playing guitar was not a good fit for me, and I quickly changed course my freshman year. Having always been interested in philosophy and politics, I turned these interests into my areas of study. I got the chance to study with some amazing professors, and was able to study a wide variety of subjects: international relations, philosophy of language, developmental economics, political theory, bioethics, and theory of mind. While I cherished the work I was able to do at VCU (plus the time I spent playing music in Richmond), I always felt it was lacking in quantitative rigor.

After I graduated I wanted to focus on making music, so I took a job as a Customer Relations Manager at Fairfax Hyundai because it would afford me with more time to work on music. The simplicity of the job also came with the difficulty of performing a repetitve task that lacked critical thinking. Craving intellectual stimulation I decided to take courses at George Mason University. Having come to doubt the prognosis of my quantitative reasoning by my high school physics teacher, I wanted to begin learning the statistics and calculus that I had missed out on in undergrad. I also wanted to learn more about economics. Having taken a required course in international economics my senior year, I knew that analyzing the complexities of social interaction but with logical rigor was a natural fit for me. Little did I know that I would enjoy it so much.

Excelling in a mathematics heavy course in Microeconomics, and an interest in the professor's research lead to a job offer. The professor was Kevin McCabe, director of the Center for the Study of Neuroeconomics at George Mason University, and he offered me a position as a Research Fellow at the Center. Jumping at the opportunity, I found myself immersed in work that was profoundly rewarding. Not only was the work intellectually stimulating, but I developed the programming and quantitative skills that provided the foundation for all my work today. It was a crash course in experimental and computational thinking, one that required me to wear many hats.

When I started at CSN, I was mainly assisting with research that was already in progress, reviewing literature, running sessions of the experiments, testing software, but soon began taking on additional roles. I was placed in charge of maintaining the center's computer systems, and made an instructor at the Center's week long course in experimental economics and programming for high school students. Soon though, I was leading my own research on Bayesian decision making, mechanism design, pricing strategies, task switching, auction design, and search behavior. I also got to lead two cohorts of undergraduates through a program we had developed with the International Foundation for Research in Experimental Economics, headed by Nobel laureate Vernon Smith. With this, I also took on the role of lead developer for the software used for experiments conducted by the Center. Developing experiments from the ground up gave me a deep understanding of economic theory, and experimental design and analysis, but also a deep appreciation of software development. I lead the charge in moving away from developing one-off experimental software, and toward designing a platform that could be used to develop experiments in python. This culminated in the python package mTree, which we presented at the 2017 annual meeting of the Economic Science Association.

While my time working at the Center lead me to pursue a doctoral degree in Economics, I realized that a life of academic research was not for me. I wanted to focus on programming, statistics, and building things that could actually help people; not readings and lectures filled with toy examples. I followed the lead of a former graduate student who had just taken a job as data scientist, and began pursuing a career in data science.

The questions that I find most interesting are those that combine my love of game theory, deep learning, and cognitive computation; particularly when it comes to graph modeling, computer vision, adversarial methods, and machine cognition, especially at the edge of the computational limitations.