About me

I am an assistant professor of biostatistics at the Institute for Informatics, Data Science, & Biostatistics at Washington University School of Medicine in St. Louis. My research integrates causal modeling and machine learning for improving the reliability of evidence generation and building responsible AI algorithms. I completed my PhD in Biomedical Informatics at Columbia University, advised by George Hripcsak and David Blei. I have a master’s degree in Computational Biology from Harvard School of Public Health and a bachelor’s degree from Boston University. Here is my CV.

My recent research has focused on addressing confounding bias in observational studies using real-world data, and explaining root causes of healthcare disparities with causal machine learning. My research interests include:

1. developing federated causal machine learning for treatment effect estimation across multiple databases without sharing patient-level data.
2. developing heterogeneous and individualized treatment effect estimation algorithms for personalized treatment planning.
3. improving the equity of health care through machine learning.

HIRING! I am looking for one postdoctoral fellow or senior data analyst. Please email me your CV, cover letter, and 2-3 publications if you are interested in working in my lab.

PhD Students I am looking to add 1-2 PhD students starting Fall 2024. Please apply directly to the Biomedical Informatics and Data Science (BIDS) PhD program at Washington University School of Medicine. In your PhD application, please explicitly mention your interest in working with Professor Linying Zhang. Existing BIDS and Computational & Systems Biology (CSB) PhD students interested in rotating through the lab should email me directly.

You can reach me at linyingz [at] wustl [dot] edu or on Twitter.

News

• Feb 5-7: I will be presenting the work on the algorithmic fairness of eGFR at AIMHC in Santa Ana, CA!
• Dec 31 2023: Our paper titled Causal Fairness Assessment of Treatment Allocation with Electronic Health Records was accepted at JBI!
• Dec 10 2023: I will be the senior chair at the causality in health roundable at Machine Learning for Health (ML4H)!
• Nov 11-15 2023: I will be at the AMIA Symposium to present our work on explaining treatment disparities with causality!
  
• Oct 2023: I started my job as an assistant professor of biostatistics at Washington University!
  
• July 2023: I successfully defended my thesis!
  
• Oct 2022: I received the OHDSI Best Community Contribution Award in Methodological Research for my work on health equity!
  

Selected Publications & Preprints

Causal machine earning for reliable real-world evidence generation in healthcare.
Linying Zhang
PhD thesis, Columbia University, 2023.
[URL]

Explaining treatment disparities from a causal perspective with EHRs.
Zhang L, Jiang X, Natarajan K, and Hripcsak G.
AMIA Symposium, 2023.
[Abs]

Causal fairness assessment of treatment allocation with electronic health records.
Zhang L, Richter LR, Wang Y, Ostropolets A, Elhadad N, Blei DM, and Hripcsak G.
JBI (to appear), 2023.
[PDF]

Adjusting for indirectly measured confounding using large-scale propensity score.
Zhang L, Wang Y, Schuemie MJ, Blei DM, and Hripcsak G.
Journal of Biomedical Informatics, 2022.
[PDF]

Evaluating and Improving the Performance and Racial Fairness of Algorithms for GFR Estimation.
Zhang L, Richter LR, Kim T, and Hripcsak G.
IEEE International Conference on Artificial Intelligence x Medicine, Health, and Care (AIMHC), 2024.
[PDF]

The Medical Deconfounder: Assessing Treatment Effects with Electronic Health Records.
Zhang L, Wang Y, Ostropolets A, Mulgrave JJ, Blei DM, and Hripcsak G.
Machine Learning for Healthcare Conference, 2019.
[PDF][code]

Data assimilation on mechanistic models of glucose metabolism predicts glycemic states in adolescents following bariatric surgery.
Richter LR, Albert BI, Zhang L, Ostropolets A, Zitsman JL, Fennoy I, Albers D, and Hripcsak G.
Frontiers in Physiology, 2022.
[PDF]

Predicting hospitalization of COVID-19 positive patients using clinician-guided machine learning methods.
Song W, Zhang L, Liu L, Sainlaire M, Karvar M, Kang M, Pullman A, Lipsitz S, Massaro A, Patil N, Jasuja R, and Dykes PC.
Journal of the American Medical Informatics Association, 2022.
[PDF]

Predicting pressure injury using nursing assessment phenotypes and machine learning methods.
Song W, Kang M, Zhang L, Jung W, Song J, Bates D, and Dykes PC.
Journal of the American Medical Informatics Association, 2021.
[PDF]

Teaching

Computational Methods. Columbia University. Spring 2020.
Computer Applications in Health Care & Biomedicine. Columbia University. Fall 2019.
Principles of Biostatistics I&II. Harvard T.H.Chan School of Public Health. Summer 2017.