Rajesh Ranganath

About

Rajesh Ranganath is an Assistant Professor at the Courant Institute at NYU in Computer Science and at the Center for Data Science. He is also part of the CILVR group. His research interests include causal, statistical, and probabilistic inference, out-of-distribution detection and generalization, deep generative modeling, interpretability, and machine learning for healthcare. Before joining NYU, he earned degrees in computer science, completing his PhD at Princeton University working with Dave Blei, and his undergraduate studies at Stanford University. He has also spent time as a research affiliate at MIT’s Institute for Medical Engineering and Science.

Research topics

• Computer Science
• Data science
• Machine Learning
• Political Science
• Artificial Intelligence
• Medicine
• Public relations
• Knowledge management
• Mathematics
• Pathology

Selected publications

• The role of machine learning in clinical research: transforming the future of evidence generation

  Trials · 2021 · 277 citations

  BACKGROUND: Interest in the application of machine learning (ML) to the design, conduct, and analysis of clinical trials has grown, but the evidence base for such applications has not been surveyed. This manuscript reviews the proceedings of a multi-stakeholder conference to discuss the current and future state of ML for clinical research. Key areas of clinical trial methodology in which ML holds particular promise and priority areas for further investigation are presented alongside a narrative…

  PublisherOA PDFDOI

• Reproducibility in machine learning for health research: Still a ways to go

  Science Translational Medicine · 2021 · 253 citations

  Machine learning for health must be reproducible to ensure reliable clinical use. We evaluated 511 scientific papers across several machine learning subfields and found that machine learning for health compared poorly to other areas regarding reproducibility metrics, such as dataset and code accessibility. We propose recommendations to address this problem.

  DOI

• AI as an intervention: improving clinical outcomes relies on a causal approach to AI development and validation

  Journal of the American Medical Informatics Association · 2025-01-07 · 19 citations

  articleOpen accessSenior author

  The primary practice of healthcare artificial intelligence (AI) starts with model development, often using state-of-the-art AI, retrospectively evaluated using metrics lifted from the AI literature like AUROC and DICE score. However, good performance on these metrics may not translate to improved clinical outcomes. Instead, we argue for a better development pipeline constructed by working backward from the end goal of positively impacting clinically relevant outcomes using AI, leading to conside…

  PublisherOA PDFDOI

• Causal Inference in Oncology: Why, What, How and When

  Clinical Oncology · 2024-07-11 · 15 citations

  reviewOpen accessSenior author

  Oncologists are faced with choosing the best treatment for each patient, based on the available evidence from randomized controlled trials (RCTs) and observational studies. RCTs provide estimates of the average effects of treatments on groups of patients, but they may not apply in many real-world scenarios where for example patients have different characteristics than the RCT participants, or where different treatment variants are considered. Causal inference defines what a treatment effect is a…

  PublisherOA PDFDOI

• When accurate prediction models yield harmful self-fulfilling prophecies

  Patterns · 2025-04-01 · 11 citations

  articleOpen access

  Prediction models are popular in medical research and practice. Many expect that by predicting patient-specific outcomes, these models have the potential to inform treatment decisions, and they are frequently lauded as instruments for personalized, data-driven healthcare. We show, however, that using prediction models for decision-making can lead to harm, even when the predictions exhibit good discrimination after deployment. These models are harmful self-fulfilling prophecies: their deployment…

  PublisherDOI

Recent grants

• Deep probabilistic predictive models for stroke and coronary heart disease

  NIH · $3.3M · 2019–2026

• Career: Building Models that Avoid Spurious Correlations through Interpretability and Representation Learning

  NSF · $547k · 2022–2027

Frequent coauthors

• Aahlad Puli

  New York University

  60 shared

• David M. Blei

  59 shared

• Mukund Sudarshan

  Courant Institute of Mathematical Sciences

  40 shared

• Marzyeh Ghassemi

  30 shared

• Neil Jethani

  30 shared

• Dustin Tran

  22 shared

• Wouter A. C. van Amsterdam

  Heidelberg University

  19 shared

• Yindalon Aphinyanaphongs

  New York University

  18 shared

Labs

• CILVR at NYUPI

Education

• B.S.

  Stanford University

• Ph.D.

  Princeton University

Awards & honors

• Best paper award: Best application paper at ICML 2009

Similar researchers at New York University

• Rob Fergush-95
• Dennis Shashah-84
• Mehryar Mohrih-77