How Machine Learning Research @ Penn supports student AI researchers
Machine Learning Research @ Penn prepares undergraduates for research by discussing academic papers in small groups, much like book clubs would dissect a novel
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Last year, researchers published over 50,000 papers on machine learning—a nearly 50% jump from the previous year. “Figuring out what’s worth reading is hard,” says Alok Shah, president of Machine Learning Research @ Penn (MLR@Penn), a club that helps students stay on top of fast-moving developments in AI.
Reading academic papers, especially about cutting-edge research, can be a tricky endeavor. Every advance in AI builds on prior work, so understanding one paper might require diving into the citations to find (and then read) many more.
“Quickly judging a paper’s technical merit and broader impact? That’s not something I learned in class,” Shah says. “It’s a skill I’ve been trying to build—and honestly, I’m still working on it.”
Class of 2024 graduate Keshav Ramji founded the club in 2023, which quickly grew from a small core to dozens of students, majoring in everything from computer science to bioengineering.
One of the main ways MLR@Penn prepares undergraduates for research is by discussing academic papers in small groups, much like book clubs would dissect a novel. “By putting our heads together,” says club member Alexander Kyimpopkin, “we’re able to understand papers better and keep up with the state of the field.”
This notion of a “reading group” is commonplace in the lives of graduate students and industry researchers but is rarely accessible to undergraduates outside of on- or off-campus internships. “It’s an effective forum to talk about the latest cutting-edge research, like you would in industry or academia, but tailored for students with less experience,” says Ramji.
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