Carey Priebe has worked with hospitals hoping to reduce stress on surgical nurses, cancer researchers trying to improve mammograms, and neuroscientists mapping connections between brain cells. He’s shown how satellites can spot Martian volcanoes and given law enforcement new tools against human traffickers, shadowy financial networks, and corporate fraud.

The athletic, 6-foot-3 professor of applied mathematics and statistics at the Whiting School of Engineering, with his calm midwestern manner, graying ponytail, and white goatee, claims no expertise in any of these fields. But he has helped professionals in all of them.

His specialty is analyzing networks—of people, neurons, organizations, you name it—using statistics and graph theory, a mathematical discipline that reduces problems to the study of points and the lines that connect them.

Graph theory dates back to the 18th century and for a long time had limited practical application. In today’s wired world, this highly abstract mathematics has become a powerful tool for defining, predicting, and comparing all kinds of things. The theory has aided the development of machine learning and artificial intelligence, which have driven the rise of tech giants like Facebook and Google.

While Priebe is just one of many working in his field, colleagues and former students say his work stands out for its rigorous theoretical approach to solving real-world problems.

He has published in Science and Nature, and held fellowships at the Alan Turing Institute in London and the Isaac Newton Institute for Mathematical Sciences at Cambridge. He has received funding from the National Science Foundation, the National Institutes of Health, the Defense Advanced Research Projects Agency, and Microsoft, and has worked with the Johns Hopkins University Applied Physics Laboratory on cyber issues as well as with a team at Cambridge University on understanding the wiring of the brain.

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