Rumi Chunara

Associate Professor of Biostatistics

Associate Professor of Computer Science and Engineering, Tandon

Director of Center for Health Data Science

Professional overview

The overarching goal of Dr. Rumi Chunara's research is to develop computational and statistical approaches for acquiring, integrating and using data to improve population-level public health. She focuses on the design and development of data mining and machine learning methods to address challenges related to data and goals of public health, as well as fairness and ethics in the design and use of data and algorithms embedded in social systems.

At NYU, Dr. Chunara also leads the Chunara Lab, which develops computational and statistical methods across data mining, natural language processing, spatio-temporal analyses and machine learning, to study population health. Previously, she was a Postdoctoral Fellow and Instructor at HealthMap and the Children's Hospital Informatics Program at Harvard Medical School. She completed her PhD at the Harvard-MIT Division of Health Sciences and Technology and BSc at Caltech.

Education

BS, Electrical Engineering (Honors), Caltech

MS, Electrical Engineering and Computer Science, MIT

PhD, Medical and Electrical Engineering, MIT (Harvard-MIT Division of Health Sciences and Technology)

Honors and awards

Max Planck Sabbatical Award (2021)

speaker at NSF Computer and Information Science and Engineering Directorate Career Proposal Writing Workshop (2020)

Invited tutorial on Public Health and Machine Learning at ACM Conference on Health, Inference and Learning (2020)

Keynote at Human Computation and Crowdsourcing (2019)

Invited Speaker at Expert Group Meeting at United Nations Population Fund, Advances in Mobile Technologies for Data Collection Panel (2019)

Keynote at ''Mapping the Equity Dimensions of Artificial Intelligence in Public Health'', University of Toronto (2019)

Facebook Research Award (2019)

Gates Foundation Grand Challenges Exploration Award (2019)

NSF CAREER Award (2019)

MIT Technology Review Top 35 Innovators Under 35 (2014)

MIT Presidential Fellow (2004)

Areas of research and study

Health Disparities

Machine learning

Social Computing

Social Determinants of Health

Publications

Publications

Why we need crowdsourced data in infectious disease surveillance

Chunara, R., Chunara, R., Smolinski, M. S., & Brownstein, J. S. (n.d.).

Publication year

2013

Journal title

Current Infectious Disease Reports

Volume

15

Issue

4

Page(s)

316-319

10.1007/s11908-013-0341-5

Abstract

Abstract

In infectious disease surveillance, public health data such as environmental, hospital, or census data have been extensively explored to create robust models of disease dynamics. However, this information is also subject to its own biases, including latency, high cost, contributor biases, and imprecise resolution. Simultaneously, new technologies including Internet and mobile phone based tools, now enable information to be garnered directly from individuals at the point of care. Here, we consider how these crowdsourced data offer the opportunity to fill gaps in and augment current epidemiological models. Challenges and methods for overcoming limitations of the data are also reviewed. As more new information sources become mature, incorporating these novel data into epidemiological frameworks will enable us to learn more about infectious disease dynamics.