The Massachusetts Institute of Technology's (MIT) AnyScale Learning For All (ALFA) group investigates a wide range of big data challenges. ALFA focuses on working with raw data that comes directly from the source and then investigates the data with a variety of techniques, most of which involve scalable machine learning and evolutionary computing algorithms. "Machine learning is very useful for retrospectively looking back at the data to help you predict the future," says ALFA director Una-May O’Reilly. "Evolutionary computation can be used in the same way, and it's particularly well suited to large-scale problems with very high dimensions." Within the evolutionary field, O'Reilly has particular interest in genetic programming. "We distribute the genetic programming algorithms over many nodes and then factor the data across the nodes," she says.  To read further, please visit http://newsoffice.mit.edu/2015/una-may-oreilly-evolutionary-approaches-big-data-problems-0114.