About

Hi! I received my PhD from Stanford in Autumn 2025, where I worked with Itai Ashlagi and Al Roth. I was grateful to be supported by the NSF GRFP and the Stanford Data Science Scholarship. My PhD research was on market design (matching optimization) using ML, simulation, and experimentation as tools to improve organ allocation. During my PhD, I interned at Lyft on matching optimization.

Prior to my PhD, I graduated from Princeton with a BSE in Computer Science with a minor in Statistics & Machine Learning. During undergrad, I interned at DeepMind, Google, and Two Sigma.

You can reach me at gzguan at stanford dot edu.

Projects

More projects

Coming soon!

Organ Allocation Research

My PhD research was on market design (matching): Improving the complex kidney allocation system with ML, optimization, simulation, and experimentation and had real-world policy impact. Highlights include:

• Developed heuristics [1] and end-to-end ML models [2, 3] to predict nonuse risk of deceased donor kidneys, developing scalable pipelines and evaluation workflows (Python)
• Reached out directly to C-suite leaders at over 15 organ procurement organizations (~25% of the nation’s total) and recruited them to a pilot switchback experiment [4], applying causal inference methods to measure the causal effect of integrating ML predictions into clinical workflows (Python)
• Developed a scalable, optimized kidney allocation simulator that modeled real-world acceptance behavior and competing risks, and leveraged market design principles to design, optimize, and test new allocation policies with the simulator [5] (Python, Pytorch, C, C++)

A subset of publications from this work:

1. Guan et al. Insights from refusal patterns for deceased donor kidney offers. Transplantation, 2025. 💡 Market deisgn blog link 💡

2. Guan, et al. Machine learning predictions for assessing hard-to-place deceased donor kidneys. Kidney Medicine, 2025.

3. Agarwal, Ashlagi, Guan, Somaini, Zou. Time-constrained decision making in deceased donor kidney allocation 🌟 NeurIPS 2022 Workshop on Learning from Time Series for Health (spotlight presentation) 🌟

4. Agarwal, Ashlagi, Guan, Somaini, Zou. Artificial Intelligence to Enhance Organ Procurement Organization Coordinators’ Decisions to Expedite. Technical Report.

5. Guan, et al. Accelerated allocation policies to improve utilization of marginal deceased donor kidneys In preparation.

• Picture from my defense!
• In April 2025, I gave a talk about this work (“How Data-driven Insights Can Inform National Organ Allocation Policy”) at Princeton Global Health Policy

Selected health policy research

I have also worked on various projects in hospital operations and health policy, leading to 8 other publications (6 first-author) in medical journals and CS conferences (see my Google Scholar).

Empirical characteristics of Affordable Care Act risk transfers
Grace Guan, advised by Mark Braverman
Princeton Computer Science Senior Thesis
🏆 Won Outstanding Computer Science Senior Thesis Prize
🏆 Won Princeton’s Choice Award at Princeton Research Day 2018

Guan and Engelhardt. Predicting sick patient volume in a pediatric outpatient setting using time series analysis. MLHC 2019: Proceedings of Machine Learning Research, 2019, 106: 271-287
🏆 Won 2020 NCWIT Collegiate Award Honorable Mention

Stretch Reminder Chrome Extension

Remember to stretch! I built a stretch reminder app that had 1000+ daily users at its peak. Stretch Reminder Chrome Extension (Beta Test Version)