In his research and in other parts of life, Ankur Moitra likes to journey off the beaten path. His explorer mentality has brought him to at least one edge of the unknown — where he seeks to determine how machine learning, used in increasingly diverse and numerous applications, actually works.
“Machine learning is eating up the world around us,” says Moitra, a theoretical computer scientist and associate professor in MIT’s Department of Mathematics, “and it works so well that it is easy to forget that we don’t know why it works.”
Moitra says he is attempting to put machine learning “on a rigorous foundation,” analyzing the methods that are currently used to put it into practice. He is also, he says, “trying to design fundamentally new algorithms that can expand our toolkit. As a byproduct, algorithms we understand rigorously can also aspire to be ones that are more robust, interpretable, and fair.”
Moitra was raised to be an independent thinker. Growing up in Niskayuna, New York, he was surrounded by a family of computer scientists. His parents encouraged him, however, to explore his many other interests.
“I decided pretty early on that computer science was definitely not cool,” he says. “But the joke was on me. Eventually I came to discover computer science and mathematics on my own and fell in love with them.”
Moitra received his bachelor’s degree in electrical and computer engineering from Cornell University in 2007. He earned his master’s and PhD from MIT in computer science, in 2009 and 2011, and joined the MIT faculty in 2013. Moitra received tenure in 2019, and is currently a principal investigator in MIT’s Computer Science and Artificial Intelligence Laboratory and a core member of the Statistics and Data Science Center.
Throughout Moitra's education, his independence only grew. He discovered that not only did he want to come up with his own answers involving algorithms and their connections with such areas as machine learning, statistics and operations research, he wanted to be the one formulating the questions.
“I realized that I do my best research when I make up my own questions,” Moitra says, “and that’s a perfect fit for theoretical machine learning where we often don’t know where to begin.”
Moitra says that in his approach to research, “every trick you can dream up is fair game. It doesn’t matter how ugly or complicated your proof gets.”
His intellectual adventurousness has drawn the admiration of colleagues and mentors along the way. When Moitra won a David and Lucile Packard Fellowship in 2016, Professor Tomasz Mrowka said, “He is the dream colleague: He is deeply intellectually curious,” and referred to his “fundamental contributions to his discipline.”
In his teaching, Moitra encourages his students to
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