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Biostatistics

Bayesian Learning and Spatio-temporal modeling (BLAST)

The growing availability of data from a variety of sources gives us opportunities to investigate public health questions we previously could not have asked. We cannot, however, extract meaningful insights without property accounting for complex structures underlying modern large-scale data. Bayesian and Spatio-temporal models are ideally suited to this task, yet major methodological and computational challenges remain in their practical deployment. This working group explores ideas and innovations necessary to meet these challenges.

Application areas of interest include, but are not limited to, precision medicine, environmental health,  disease epidemiology, genomics, healthcare analytics, etc.

For further information and/or to sign up for the mailing list, contact the group leaders Abhi Datta at abhidatta@jhu.edu and Aki Nishimura at aki.nishimura@jhu.edu