Recovering Information from High-Dimensional Data 

Making good decisions in business, or identifying threats in communication streams, requires us to glean meaningful information from data. Data sets are often large, messy, noisy, and high-dimensional, so this is not an easy thing to do! Information is usually hidden and we have to work hard to extract it. This project explores data sets using computational tools from linear algebra. The aim is to identify hidden structure in data and interpret its meaning.