In his research and in other areas of life, Ankur Moitra likes to move off the beaten track. His exploratory mentality has at least taken him to the edge of the unknown – where he is trying to figure out how machine learning, which is used in increasingly diverse and numerous applications, actually works.
“Machine learning is eating up the world around us,” says Moitra, theoretical computer scientist and associate professor in MIT’s Department of Mathematics, “and it works so well it’s easy to forget that we don’t know why it works. ”
Moitra says he is trying to “put machine learning on a rigorous foundation” by analyzing the methodologies that are currently being used to put it into practice. Also, he says, “he’s trying to create fundamentally new algorithms that can extend our toolkit. As a by-product, algorithms that we rigorously understand can also be more robust, interpretable and fairer. “
Moitra was raised to be an independent thinker. Growing up in Niskayuna, New York, he was surrounded by a family of computer scientists. However, his parents encouraged him to explore his many other interests.
“I realized early on that computer science was definitely not cool,” he says. “But the joke was on me. At some point I discovered 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 masters and PhDs in computer science from MIT in 2009 and 2011, and joined the MIT faculty in 2013. Moitra was hired in 2019 and is currently a senior researcher in MIT’s Computer Science and Artificial Intelligence Laboratory and a core member of the Statistics and Data Science Center.
During Moitra’s education, his independence only grew. He realized that he not only wanted to give his own answers about algorithms and their connections to areas such as machine learning, statistics and operations research, but also wanted to formulate the questions.
“I realized that the best way to do my research is to formulate my own questions,” says Moitra, “and that fits perfectly with theoretical machine learning, where we often don’t know where to start.”
Moitra says in his research approach: “Every trick you can think of is fair game. It doesn’t matter how ugly or complicated your proof gets. “
His intellectual thirst for adventure has drawn admiration from colleagues and mentors. When Moitra won Professor Tomasz Mrowka, who received a David and Lucile Packard Fellowship in 2016, 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 students to move from “safe areas where other researchers have laid the groundwork and asked the right questions that you want to answer now”.
On the other hand, he mitigates this freedom of movement when teaching or giving lectures: “I think about how easily I can do something and whether there are some practical examples that bring it home.”
This teaching approach is well received. In 2018, Moitra won a teaching award from the School of Science for his graduate course 18,408 (Algorithmic Aspects of Machine Learning). His nominees called him an “inspiring, caring, and engaging” teacher.
Moitra says MIT is an excellent environment for him.
“Everyone is bursting with energy and looking forward to making the world a better place,” he says. “It’s contagious.”
Between his teaching duties and the time with his wife and two children – with a little time off to play or watch sports – Moitra’s schedule is full. He thinks best late at night when he’s alone, he says.
“Every now and then I get so obsessed with a problem and feel like it’s so close to being resolved that I just can’t sleep,” he says. “I stay up for hours pacing up and down the house. As a professor, your inbox is always flooded and your schedule is always crammed with meetings. But nobody needs you at night and everyone is sound asleep and I can think deeply without distraction. “
During these hours Moitra can “break new ground” and wander freely, sometimes making discoveries that become new areas of research.
“There are really fundamental, fundamental questions that are exciting and that no one has dared to ask,” he says. “And when you discover something new like this, it is a particular pleasure when other people go on the expedition.”