Building Better Tools for the Big Data Boom

WID researchers are engaging in fundamental mathematical and statistical research to support the development, testing, and fine-tuning of tools for the future, finding new ways to make sense of the mountains of data that are available in the 21st century and bringing into view important applications on the horizon.

Po-Ling Loh

Po-Ling Loh

Assistant Professor

  • WID Affiliate

High-dimensional statistics, compressed sensing, nonconvex optimization, robust statistics, and network inference.

Page Lab

Algorithms for data mining and machine learning, and their applications to biomedical data, especially clinical and high-throughput genetic and other molecular data. Of particular interest are inductive logic programming (ILP) and other multi-relational learning techniques capable of dealing with complex data points (such as molecules or clinical histories) and producing logical rules.