Welcome to the Lab Page of Rumi Chunara, Assistant Professor at New York University.
The overarching goal of our research is to develop the principles needed to incorporate unstructured data into a better understanding of our health. We primarily develop computational and statistical methods across data mining, natural language processing, spatio-temporal analyses and machine learning, to study population-level public health.
**Graduate Assistant deep learning position available – see description below**
*New paper on Assessing Behavioral Stages From Social Media Data accepted for ACM CSCW 2017. Congratulations Jason!
*New paper on Network Inference from Multimodal data: A Review of Approaches from Infectious Disease Transmission (Journal of Biomedical Informatics). Congratulations Bisakha! 09/2016
*New NSF grant on Combining Community and Clinical Data for Augmenting Influenza Modeling with Dr. Shaman‘s group from Columbia! Awarded 09/2016
*Prof. Chunara to give an invited talk at Personalized Health in the Digital Age symposium at Campus Biotech in Switzerland September 2016
*Prof. Chunara to give an invited talk at CCS ’16 workshop on Digital Epidemiology and Surveillance September 2016
*Chunaralab undergrad researcher William Herrera accepted to the prestigious NIH Summer Internship Program in Biomedical Research.
*Nabeel Rehman will present his work on Using Propensity Score Matching to Understand the Relationship Between Online Health Information Sources and Vaccination Sentiment at the AAAI Spring Symposium at Stanford University in March 2016.
*Prof. Chunara was an invited speaker at a panel on Web-Based Public Health Informatics at IEEE BHI ’16 in February 2016.
*Bisakha Ray presented her work on Integrating Genomic Data in a Community-based Multi-modal Viral Transmission Model for Network Inference at the NIPS Workshop on ML for Healthcare in Montreal, December 2015.
Watch the below video to learn about our research mission:
Graduate Assistant Position: The laboratory of Dr. Rumi Chunara at NYU Tandon CSE and the College of Global Public Health is seeking a motivated graduate assistant to develop and study advanced computational methods to understand population genetic structure in human and animal organisms. Our group has a specific project already under way with potential for significant biological findings, and close to publication. The researcher will help refine the algorithms, polish the work and contribute to publication.