Population Informatics
Welcome
The Population Informatics Lab applies informatics, data science, and computational methods to the increasingly large digital traces available to advance public health, social science, and population research. This research group is a joint effort between the School of Public Health, the Departments of Computer Science and Engineering, and Industrial Systems and Engineering at Texas A&M University. We specialize in data science, KDD (Knowledge Discovery and Datamining), data integration, visualization, decision support systems, health informatics, computational social science, data governance, and privacy with a focus on collaborating with government agencies and administrative data.
Current Events from the Lab!
Congratulate Regan's Post-doc award!
Takeaways from the International Population Data Linkage Network (IPDLN) conference in Chicago, Illinois and the photos from the conference
Dr. Kum's research on the sustained growth of Telemedicine among Texas Medicaid patients was featured by Vital Record, Texas A&M Health's News Source!
Great Job to everyone here at the lab! Our $12.6 Million Award was featured by Vital Record, allowing us to continue our work evaluating Medicaid and its impact on low-income Texans!
We are excited to share that Michelle Hayek, after winning the 2023 Student Seed Fund Award from PATHS-UP and the National Science Foundation (NSF), presented her work in a poster at the 2024 Digital Health Workshop at Rice University—and was honored with an award for her outstanding research!
Announcements
New Fundings and Achievements:
Funding: Texas 1115 Medicaid Waiver Ten Year Extension Evaluation (9/1/2023-831/2032). $12M. PI Kum, Shipp, & Ohsfeldt.
Dr. Kum cochair the Digital Ethology Ernst Strüngmann forum (Forum is supported by the Deutsche Forschungsgemeinschaft, the German Research Foundation). Book in print by MIT press (summer 2024).
What is Population Informatics?
Computational Social Science is an emerging research area at the intersection of health science, social sciences, computer science, and statistics in which quantitative methods and computational tools are applied to big data about people to answer social science questions. Broadly speaking there are two approaches as follows:
Population Informatics : The systematic study of populations via secondary analysis of massive data collections (termed “big data”) about people. In particular, we focus most on improving health outcomes for a population and the data science approach which is about generating actionable information from raw data. Another important aspect of population informatics is Public health informatics which is more about how to best utilize the information generated using data science to improve public health.
Simulations (i.e., Agent Based Modeling (ABM) ) : Discover useful information and knowledge about our society through simulating the actions and interactions of autonomous agents (individuals and groups/organizations). Many of the parameters to model autonomous agents come from Population Informatics research.
Foundational Publications:
Ragan, E., Kum, H.-C., Ilangovan, G., and Wang, H. (2018). Balancing Privacy and Information Disclosure in Interactive Record Linkage with Visual Masking. Proceedings of the SIGCHI conference on Human factors in computing systems. ACM. CHI2018 Honourable Mention Award (top 5% of all submissions). Also invited to be presented at the Fourteenth Symposium on Usable Privacy and Security (SOUPS) Aug 2018 as a poster.