Rumi Chunara

Associate Professor, Biostatistics and Computer Science & Engineering

Director, Center for Health Data Science

New York University, School of Global Public Health and
Tandon School of Engineering

Publications | Group | Teaching | Awards | Press

I am an Associate Professor of Computer Science, Biostatistics, and Global Public Health at New York University, where I direct the Center for Health Data Science (CHDS). My research sits at the intersection of machine learning, public health, and society. I focus on understanding what makes algorithms actually work in practice for healthcare and health systems—how methods must be designed, tested, and deployed so that they are useful, reliable, and actionable.
My group develops methods in causal inference (e.g., showing how missing mediators affect transportability of causal effects, arXiv 2024), domain adaptation (demonstrating the challenges of transfer learning across urban and rural contexts and introducing fairness-improving strategies, CVPR 2022), representation learning (introducing a novel augmentation approach for greenspace detection in underrepresented areas, ACM JCSS 2025), and multimodal modeling (linking satellite imagery with health and climate data to measure urban exposures, ACM JCSS 2025).


We work closely with real-world partners—including insurers, health systems such as NYC Health + Hospitals, and city planning agencies—to ensure our methods are grounded in practice. For example, our algorithms have been integrated into safety-net hospitals to mitigate bias in predictive models, and our geospatial work on greenspace is informing the Karachi 2047 Regional Plan.
In addition to advancing methods, I am committed to championing the science of how to “think structurally” with data. This includes leading the Data Science for Social Determinants Community of Practice, which brings together researchers and practitioners globally to better capture, organize, and model social and environmental determinants of health.
My work has been recognized with awards including [e.g., NSF CAREER, Max Planck Society Sabbatical award, etc.], and my students have received prestigious recognitions such as the Google PhD Fellowship. Lab alumni have gone on to roles in academia, health systems, and technology companies including Amazon, Google, and Kaiser Health.
In recent years, I have also developed a body of work related to cardiovascular disease (JACC 2023, Prog Card Dis 2023, Prev Med 2022) and best practices for teaching in Health Data Science (Harvard Data Sci Review 2022, Lancet Global Health 2023). Please see my Google Scholar page for a full list of publications.


*Updates*
*My recent OpEd in The Chronicle of Higher Education on how Scholars need to work together across disciplines to shape more-ethical AI systems. Flaws in AI Are Deciding Your Future. Here's How to Fix Them
*See our new paper in PNAS on how Utilizing big data without domain knowledge impacts public health decision-making
*See our piece in The Lancet Digital Health on The Need for Data Science Methods and Capacity for Incorporating Social Determinants of Health into analytic models
*We are launching a new Center for Health Data Science at NYU. Stay tuned for more!
*I gave a lecture as part of the Suessmilch Lecture series at the Max Planck Institute for Demographic Research on "Using Data to Advance the Science of Health Disparities". Gave a similar talk at the USC Institute on Inequalities in Global Health
*I am the PI of a new training program from the NIH: the NYU-Moi Data Science for Social Determinants Training Program

Selected Awards and Honors

Selected Press