The Applied Analytics Cluster consists of researchers and students who not only utilize current data mining and machine learning techniques, but seek to explore innovative applications of such methods with large scale data (such as genomics, phenomics, remote sensing). In particular, members of our cluster seek to use innovative techniques and data driven models to improve predictions relevant to their disciplines, such as medical diagnostics, climate projections among others. Members of this cluster (who are often generating or collecting data themselves) are generally interested in potential collaborations with members of other clusters who are developing new tools and algorithms.

Broadly, data harmonization and integration describes the work that is necessary to enable data from different sources to be combined. This sort of work can be necessary to bring together variables in a single dataset or database or, indeed, to enable the utilization of multiple datasets or databases.

Research data management refers to a wide range of topics that are crucial for data science. These include generation of effective metadata, data storage, data security, data cleaning, appropriate use of data, and data ethics.