UK Mathematician Wins Richest Prize in Academia For His Work On Stochastic Analysis

Lanodonal writes: A mathematician who tamed a nightmarish family of equations that behave so badly they make no sense has won the most lucrative prize in academia. Martin Hairer, an Austrian-British researcher at Imperial College London, is the winner of the 2021 Breakthrough prize for mathematics, an annual $3m award that has come to rival the Nobels in terms of kudos and prestige. Hairer landed the prize for his work on stochastic analysis, a field that describes how random effects turn the maths of things like stirring a cup of tea, the growth of a forest fire, or the spread of a water droplet that has fallen on a tissue into a fiendishly complex problem. His major work, a 180-page treatise that introduced the world to “regularity structures,” so stunned his colleagues that one suggested it must have been transmitted to Hairer by a more intelligent alien civilisation. Hairer, who rents a London flat with his wife and fellow Imperial mathematician, Xue-Mei Li, heard he had won the prize in a Skype call while the UK was still in lockdown. “It was completely unexpected,” he said. “I didn’t think about it at all, so it was a complete shock. We couldn’t go out or anything, so we celebrated at home.” The award is one of several Breakthrough prizes announced each year by a foundation set up by the Israeli-Russian investor Yuri Milner and Facebook’s Mark Zuckerberg. A committee of previous recipients chooses the winners who are all leading lights in mathematics and the sciences. Other winners announced on Thursday include a Hong Kong scientist, Dennis Lo, who was inspired by a 3D Harry Potter movie to develop a test for genetic mutations in DNA shed by unborn babies, and a team of physicists whose experiments revealed that if extra dimensions of reality exist, they are curled up smaller than a third of a hair’s width.

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https://science.slashdot.org/story/20/09/11/199230/uk-mathematician-wins-richest-prize-in-academia-for-his-work-on-stochastic-analysis?utm_source=rss1.0mainlinkanon&utm_medium=feed