Once you're known as a good teacher, students reward you in many ways. That's the most gratifying thing.

Indranil Brahma

"When I came here, it was clear that Bucknell required you to be an excellent teacher. I was not an excellent teacher," says Professor Indranil Brahma, mechanical engineering. "But I found that there were a lot of good teaching mentors and opportunities for improving your teaching, and I took advantage of that. Now I've developed into a more balanced teacher-researcher."

Brahma came to the U.S. from India in 2000 to continue his education. He earned his doctorate in 2004 and began a career as a corporate researcher, but he felt unfulfilled. He had never intended to pursue education as a profession, but once he began to supplement his research by teaching an occasional class, he knew that he had chosen the right path.

"If I don't do research, I don't feel creative and spontaneous in the classroom," says Brahma, who has been teaching at Bucknell since 2009. "And if I don't do teaching, I tend to be obsess over little research details instead of pursuing broad questions. I need that balance of teaching and research to do both of those jobs well. Personally, I find teaching and research to be inextricably connected."

Lacking classroom experience early in his career, Brahma found many resources at Bucknell that help faculty develop as teacher-scholars. He visited the Teaching & Learning Center every week to learn classroom techniques, focused on student feedback and spent time with experienced faculty mentors.

"Once you're known as a good teacher, students reward you in many ways. They want to do independent research with you, they want to do summer research, they want to sit in on your classes," Brahma says. "That's the most gratifying thing."

While much of Brahma's research has involved engine systems, his current work focuses more broadly on the abstract problem of data mining within an engineering framework. He develops algorithms to model complex phenomena using physical laws to enhance data-driven models. These phenomena are typically hard to model using known physical laws. "Students enjoy and learn from building and performing experiments on these complex systems," he says. "Most of my recent and current research projects completely depend on student researchers.

"Most engineering systems nowadays generate oceans of data, but we hardly use this data to understand the system better," Brahma continues. "Data is mainly used in a purely empirical manner, outside the framework of physical laws. This is because data mining was initially developed from non-engineering datasets that did not follow physical laws. I try to develop algorithms that use physical laws to extract knowledge from data.

"It's a very broad field in its infancy. Everybody and everything uses models these days, so it's important as well."

Updated Sept. 30, 2016

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