I am a final year PhD student at Stanford MS&E, co-advised by Itai Ashlagi and Margaret Brandeau. My research is broadly in applied data science (ML and experimentation) and market design with the goal of working with stakeholders to inform policy decisions. I have become increasingly interested in other applications of AI in marketplaces and the impacts that it will have on society.
In summer 2024, I interned at Lyft, where I developed a matching algorithm intervention and designed an A/B test, resulting in a successfully shipped feature. Prior to my PhD, I graduated from Princeton with a BS in Computer Science with minors in Global Health Policy and Statistics & Machine Learning. During undergrad, I interned at DeepMind, Google, and Two Sigma.
I’m grateful to be supported by the NSF GRFP, the Stanford Data Science Scholarship (2023-2025), and the Stanford Technology & Racial Equity Graduate Fellowship (2022-2023).
In my free time, you can find me weightlifting, swimming, and hiking. I also enjoy going to museums and learning about art history. I was a tour guide and student outreach chair for the Princeton University Art Museum and have written many blog posts about art.
You can reach me at gzguan at stanford dot edu.
Updates
April 2025 Gave a talk “How Data-driven Insights Can Inform National Organ Allocation Policy” at Princeton Global Health Policy (link)
May 2024 “Transplant surgeons already account for inaccuracies in the Kidney Donor Profile Index (KDPI) calculation” has been published in Clinical Transplantation!
January 2024 Our paper “Resource utilization and costs associated with approaches to identify infants with early onset sepsis” has been published in Medical Decision Making: Policy & Practice!
December 2022 Presented part of our machine learning for deceased donor kidney allocation project at NeurIPS 2022 Workshop on Learning from Time Series for Health as a spotlight presentation ✨
Other Projects
- Stretch Reminder Chrome Extension (Beta Test Version) (1000+ daily users!)