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.

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: 

• •   Kum, H.C., Illangovan, G., Li, Q., Ferdinand, A.G., Ramezani, M., Giannouchos, T., and Schmit, C. School of Public Health leads development of database linkage tool that strengthens both data privacy and research accuracy. Vital Record NEWS from Texas A&M University issue Oct 24,2025. 

  • Kum, H.C., Krishnamurthy A., Machanavajjhala A., and Ahalt S. Social Genome: Putting Big Data to Work for Population Informatics. IEEE Computer Special Outlook Issue. Jan 2014

  • 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.

  • Kum, H.C., Krishnamurthy A., Machanavajjhala A., Reiter M., and Ahalt S. Privacy Preserving Interactive Record Linkage (PPIRL). J Am Med Inform. Assoc. doi:10.1136/amiajno-2013-002165

  • Kum, H.-C., Stewart, C.J.*, Rose, R.A. & Duncan, D.F., Using big data for evidence based governance in child welfare, Children and Youth Services Review (2015), Volume 58, November 2015, Pages 127-136, ISSN 0190-7409, doi: 10.1016/j.childyouth.2015.09.014.

  • Kum, H.C., and Ahalt, S. (2013). Privacy by Design: Understanding Data Access Models for Secondary Data, American Medical Informatics Association (AMIA) joint summits on translation science: clinical research informatics. Nominated for best paper award

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