Sherri Rose

Timeline

1. 2005–2013

   Statistics and biostatistics training

   She grows up in a low-income household. Her education includes a BS in statistics from The George Washington University in 2005 and a PhD in biostatistics from the University of California, Berkeley in 2011. In 2013, she is an NSF postdoctoral fellow in biostatistics at Johns Hopkins University.

2. 2011

   The first Targeted Learning book

   In 2011, she coauthors Targeted Learning: Causal Inference for Observational and Experimental Data with Mark van der Laan. The book introduces super learning and targeted maximum likelihood estimation for researchers who study causal effects.

3. 2013–2016

   A Harvard data science lab

   She joins Harvard Medical School’s Department of Health Care Policy as an assistant professor in 2013 and becomes an associate professor in 2016. With Laura Hatfield, she founds the Health Policy Data Science Lab in 2015 to study policy questions using statistical methods and machine learning.

4. 2017–2018

   New Innovator and second book

   In 2017, she receives an NIH Director’s New Innovator Award for a project on machine learning for health outcomes in low-income populations. The project develops methods to generalize results from experimental and quasi-experimental studies. With Mark van der Laan, she publishes Targeted Learning in Data Science: Causal Inference for Complex Longitudinal Studies in 2018, applying machine learning and targeted estimation to longitudinal data.

5. 2019–2023

   Biostatistics and fair regression

   From 2019 to 2023, she serves as co-editor-in-chief of Biostatistics. In 2019, she and Anna Zink release the preprint Fair Regression for Health Care Spending, which develops regression methods that include fairness in the objective function used to predict health insurance spending.

6. August 2020

   Joining Stanford Health Policy

   In August 2020, she joins the tenured faculty of Stanford Health Policy as an associate professor of medicine, moving from Harvard Medical School.

7. 2021–2022

   Spiegelman and Pioneer awards

   She receives the American Public Health Association’s Mortimer Spiegelman Award in 2021. In 2022, an NIH Director’s Pioneer Award supports her research on the social effects of health care algorithms, including potential harms to marginalized groups. The project uses causal network models and draws on machine learning, ethical AI, health economics, health policy and statistics.

8. 2024

   Reporting guidelines for clinical AI

   In 2024, she helps develop TRIPOD+AI, updated reporting guidelines for clinical prediction models that include machine learning and AI methods. The international team presents a 27-item checklist and separate guidance for abstracts to improve how researchers report prediction studies.

9. September 2025

   Fairer Medicare payment algorithms

   In September 2025, she coauthors Increasing Fairness in Medicare Payment Algorithms with Marissa Reitsma and Thomas McGuire. The work evaluates constrained regression and post-processing using enrollment and claims data from more than 4.3 million beneficiaries to improve fairness in Medicare Advantage plan payments.

10. As of October 2026

    Professor of health policy

    As of October 2026, she is Professor of Health Policy and, by courtesy, of Computer Science at Stanford University, where she directs the Health Policy Data Science Lab. Her research develops statistical machine learning methods to improve health and health equity, including work on health care algorithms, risk adjustment and chronic kidney disease.