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 the intersection of machine learning and market design, and my current research focuses on leveraging the vast amount of data that exists today to improve equity and efficiency in healthcare markets 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 Columbia Business School, 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.
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)
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 ✨