Research in HIV continues to generate highly complex data structures. Examples include genomic sequences (both host and virus); individual medical records, which include such complications as irregular measurement, missing data, and unstructured text fields; medical images; social network data; and aggregated ‘super cohorts’ such as those coordinated by the IeDEA and CNICS consortia. Even the design and analysis of randomized trials require innovative techniques to enable optimal use of data that can be expensive and labor-intensive to collect.

This symposium is designed to bring together statistical and data science researchers either working directly in the area of HIV or whose work has direct relevance to problems and data structures encountered in HIV research. We are particularly interested in engaging data science researchers in fields such as computer science, engineering, and applied mathematics, whose work in related areas might lead to innovative new approaches.

Participants will gather for focused activities related to dissemination of new methods, formation of new collaborations, extended discussion to identify new challenges, and engagement of junior investigators. Finally, owing to investments by NIH and other funding agencies, the number of HIV-focused statisticians and data scientists from low- and middle-income countries is growing. The symposium also is designed to promote continued engagement between statistical scientists from the ‘global north’ and ‘global south’.