Robust Learning from Big and Messy Data

Big data is only getting bigger. For example, the upcoming Square Kilometre Array alone will daily generate twice the amount of data sent around the Internet per day. Big data is also getting messier: incomplete, sparse, noisy, biased, and with outliers. Exploitation of these big and messy data increasingly depends on our ability to identify patterns that summarize these datasets. I will present our recent theoretical findings to learn linear and non-linear patterns from big and messy data.