Grace Guan

I am a fourth year PhD student in Management Science and Engineering at Stanford, working with Margaret Brandeau and Itai Ashlagi. I’m interested in using data science and machine learning to solve operations and market design problems in healthcare while informing policy decisions. 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 2020, I graduated from Princeton with a BS in Computer Science with minors in Global Health & Health Policy and Statistics & Machine Learning, where I was fortunate to work with Mark Braverman and Barbara Engelhardt. I formerly interned at DeepMind Health, Google, and Two Sigma.

In my free time, you can find me weightlifting, swimming, and hiking.

You can reach me at gzguan at stanford dot edu.

Updates

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! (link to twitter thread)
October 2023 Excited to present our machine learning for deceased donor kidney allocation project at INFORMS Annual Meeting in Phoenix this October (link to session info)
September 2023 Looking forward to being a data science scholar for the next two years! Also super excited to be a part of the 2023b class of the Google CS research mentorship program
July 2023 Thrilled to chair a session on organ allocation modeling and policy and present our machine learning for deceased donor kidney allocation project at INFORMS Healthcare
June 2023 Attended the Mathematics and Computer Science of Market and Mechanism Design summer school at MSRI
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 ✨