I am a senior applied scientist in a new AI team formed in 2025 at Amazon, focused on training foundational models and building LLM agents in the space of advertising. I am currently the lead scientist in developing an external-facing conversational agent for one of our core products. Previously, I was in Amazon Advertising's Demand Side Platform team, where I helped launch the team's first deep learning conversion prediction and pre-ranking models. I also led the design, research, and launch of a bandit algorithm in the bidding process as the first exploration mechanism in the system.
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 research was at the intersection of applied machine learning and epidemiology & pharmacogenomics, which is summarized by this dissertation.
Interest wise, I love both the theory behind ML, optimization, reinforcement learning, and the practical building of AI systems. I am currently writing a book about LLM agents.
"Nature cannot be fooled." - Richard Feynman
I grew up in Taichung, Taiwan as a third culture kid, and was always a curious person. However, because my high school education emphasized the liberal arts and natural sciences, I was not aware of engineering as a career until college. My bias towards the natural sciences led me to pursue pre-med as a college student. It was not until the medical school interviews when I truly realized that my true academic passion was in the quantitative fields, such as applied math, statistics, and computer science. I made a sharp career pivot thereafter, and instead pursued a Ph.D. in Computational Biology at UC Berkeley, which helped open the door into a career in AI and machine learning. 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 the spirit of the Feynman technique most useful in improving my understanding of materials. Below are blog posts that spell out the math derivations behind some popular topics in statistics and machine learning, along with their practical implementation and evaluation. Feedback and corrections via email welcome.