Teaching people to code, and making coding more accessible to everyone, is something I've been passionate about since discovering for myself the thrill—and the many challenges—of solving problems with code. As a tutor in college, I helped middle and high schoolers learn the basics of hello, world. While working as an engineer at Dropbox, I volunteered with Dev/Mission to help underserved populations in San Francisco find opportunities in tech. And in recent years, I've tried to make teaching and mentoring coding skills a part of my graduate school career, in ways big and small. I was an instructor for the UCSD Computational Social Science core (CSS 2) and a TA for the introductory coding class in the CSS program (CSS 1). For the first two years of graduate school, I lead the Psychology Data Science Club, a weekly lunch series where graduate students presented on topics and experiences that aimed to help other grad students increase their technical and quantitative skills. And for the past three years, I've served as the internal Stats Advisor for the UCSD Psychology department, a paid role in which I consult with other grad students to help debug their R code, plan their analyses, and launch web experiments. My experiences guiding others through the infinite ways code can break has also inspired my research; a recent line of work with Soohyun Liao and several members of the UCSD cognitive tools lab explores how to predict student performance in a large online dataset from data science and statistics courses throughout the US. Stay tuned for more!
In Spring, 2022 I was an instructor for the second-quarter course in the UCSD Computational Social Science undergraduate core series.
This course was a broad overview of modeling principles found in data science and computational social science settings.