I am an applied scientist at Amazon Advertising, working on machine learning model development and deployment, from research papers to writing production code. I was a major contributor in the launch of Amazon Demand-side Platform's first deep learning system. My areas of applied research include semi-supervised learning, multitask learning, and modeled uncertainty.
I completed my Ph.D. in Computational Biology at UC Berkeley in 2020, advised by Haiyan Huang and Lisa Barcellos, and supported by the NSF Graduate Research Fellowship. My graduate work was at the intersection of applied machine learning and epidemiology/pharmacogenomics, which is summarized by this dissertation.
My graduate coursework was focused on the areas of statistics and computer science, where I studied optimization, algorithms, theoretical statistics, machine learning, computer vision, NLP, and reinforcement learning.
"Nature cannot be fooled." - Richard Feynman
I grew up in Taichung, Taiwan as a third culture kid, and always had an interest in STEM. Unfortunately, because my high school education was focused on the liberal arts and natural sciences, I was not aware of the engineering career until my senior year in high school. In college, I was pre-med because I thought medicine was the best way to practice my interest in STEM. However, after trying out a few courses in statistics and programming, and going on medical school interviews, I realized I was more interested in a career in computing, engineering, and data science. This led me to turn down my medical school admissions offer and to pursue a Ph.D. in Computational Biology, where I fell in love with computing and found my eventual career path. If you happen to find my rate my professors profile, it is a joke started by college classmates back in organic chemistry. The map indicates cities I have been to.
Calvin Chi, Olivia Solomon, Caroline Shiboski, Kimberly E. Taylor, Hong Quach, Diana Quach, Lisa F. Barcellos, Lindsey A. Criswell
PLoS ONE 18(3) 2023
Calvin Chi, Kimberly E. Taylor, Hong Quach, Diana Quach, Lindsey A. Criswell, Lisa F. Barcellos
PLoS ONE 16(4) 2021
Calvin Chi, Yuting Ye, Bin Chen, Haiyan Huang
Bioinformatics 2021
Calvin Chi, Xiaorong Shao, Brooke Rhead, Evangelina Gonzalez, Jessica B. Smith, Anny H. Xiang, Jennifer Graves, Amy Waldman, Timothy Lotze, Teri Schreiner, Bianca Weinstock-Guttman, Gregory Aaen, Jan-Mendelt Tillema, Jayne Ness, et al.
PLoS Genetics 15(1) 2019
During graduate school, I found it helpful to actively take notes on what I learned, in the same spirit as the Feynman technique. These notes include mathematical derivations and re-implementation of well-known algorithms, and are useful when I find existing resources inadequate in providing a good understanding. I continue to write notes as a life-long learner, and am posting these notes in case they are helpful. Feedback and corrections via email welcome.