From the increasing information transmitted through telecommunications systems to that analyzed by financial institutions or gathered by search engines and social networks, so-called “big data” is becoming a huge feature of modern life.

But to analyze all of this incoming data, we need to be able to separate the important information from the surrounding noise. This requires the use of increasingly sophisticated techniques.

Alice Guionnet, a professor of mathematics at MIT, investigates methods to make sense of huge data sets, to find the hidden correlations between apparently random pieces of information, their typical behavior, and random fluctuations. “I consider things called matrices, where you have an array of data,” Guionnet says. “So you take some data at random, put it in a big array, and then try to understand how to analyze it, for example to subtract the noise.”

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