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Monday, January 22, 2018

From undisciplined to interdisciplinary | MIT News

"Math professor Philippe Rigollet, once a “not very disciplined” student, moves between computer science and statistics" notes Larry Hardesty, computer science and technology writer at the MIT News Office.

Originally from the small town of Cluny, in the Burgundy region of France, Philippe Rigollet made the move to MIT in 2014.
Photo: Bryce Vickmark

In 1996, when he was a high school senior in the small town of Cluny, in the Burgundy region of France, Philippe Rigollet applied to several of the two-year preparatory schools that most French students attend before moving on to university. His transcript reported a stellar math grade of 19.5 out of 20, but in the small space allotted for comments, his math teacher had written “fainéant.”

Rigollet translates that word as “slacker.”

“They were really looking for slackers in those preparatory schools,” Rigollet says. “They didn’t want people who were burned out at the end of high school and couldn’t push it, because it was much harder.”

“Slacker” is not an epithet that people tend to associate with MIT professors, and Rigollet was tenured in the Department of Mathematics last year. He is also part of MIT’s Institute for Data, Systems, and Society. But in high school, Rigollet says, “I was not very disciplined about learning stuff I didn’t want to learn.” 

Fortunately, there’s a lot that he has wanted to learn. His work is notable for its interdisciplinarity, moving back and forth between the fields of statistics and computer science and bringing insights from each to the other.

Rigollet was born in a rural French town with a population of only 365. His mother was a speech therapist, and his father taught grades two through five at the local elementary school. The 30-odd students in those four grades shared a single classroom, and during math class, Rigollet’s father would pose questions to each group in turn.

“That’s where I got used to being good at math,” Rigollet says. “I would try to listen to the harder questions from the upper class.”

The community was predominantly agrarian — “Raising chickens was a big thing,” Rigollet says — but his parents had a side line in door-to-door sales of health, beauty, and home-care products for Amway. Starting when Rigollet was 4, the family would attend Amway workshops in the U.S. for a week or two almost every year.

“That balanced out somehow the fact that I had a pretty limited perspective from where I grew up — the fact that I got to visit the United States,” Rigollet says.

“Going to the mall, having Taco Bell, it was just a dream for me.”

It also explains why, despite being educated entirely in France, Rigollet speaks such fluid, idiomatic English. “My first full sentence was ‘Can I have change for the game room?’” he says.

Mathematical freedom
On the strength of his placement exams, Rigollet earned a spot at a prestigious preparatory school in Lyon, which specialized in math and physics. He still had difficulty making himself learn stuff he didn’t want to learn, however: He excelled in math, but in physics, “I was just getting by,” he says.

“In physics, the rules were set a little too strongly for me,” he says. “Math allowed you more to have your own proof or your own way of thinking. It’s funny, because some people look for structure in math, and I’m looking for freedom. In what I’m doing now, I choose the model I want, and I do the math I want, and I do the description I want of these things.”...

For a statistician with an interest in computer science, however, a department of operations research and financial engineering was never a perfect fit. So in 2015, Rigollet moved to MIT. There he has continued to pursue parallel research tracks in pure statistics and machine learning. Some of his earliest work at MIT concerned statistical methods that could be used to optimize both the design of clinical trials and the targeting of ads to web users. More recently, he’s been investigating statistical techniques for interpreting data produced by the imaging technique known as cryoelectron microscopy, whose inventors were awarded the 2017 Nobel Prize in Chemistry.
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Source: MIT News


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