DSG Seminar Series • Citizen-Sourced Data for Public Health Modeling

Rumi Chunara, Computer Science and in Global Public Health
New York University

Knowledge generation through crowdsourcing is becoming increasingly possible and useful in many domain areas; yet requires new method development given the observational, unstructured and noisy nature of citizen-sourced data. In this talk I will discuss statistical and machine learning methods we are developing to integrate crowdsourced data into public health models. This includes, combining citizen-sourced and clinical data, accounting for biases, drawing inference from observational data, and generating relevant features. Examples will use empirical data from local and worldwide contexts.

Presentation slides (PDF)

Video of lecture (mp4)

Rumi Chunara is an Assistant Professor at New York University, jointly appointed in Computer Science and in Global Public Health. Her research interests combine data mining and machine learning with social and ubiquitous computing. Specifically she focuses on feature extraction from and statistical modeling of unstructured and observational personally-generated data — for epidemiological applications. She received her Ph.D. from MIT and was named an MIT Technology Review Innovator Under 35 in 2014.