Rising Stars Workshop Engages Diverse Voices in Machine Learning

With a goal of advancing broader inclusion and diversity in the tech world, the University of Maryland Center for Machine Learning recently hosted an annual workshop designed to empower and engage early-stage researchers from underrepresented backgrounds in computer science and machine learning.

Now in its sixth year, the Rising Stars in Machine Learning program provides a $500 honorarium and an all-expenses-paid trip to College Park, where selected scholars—a mixture of graduate students, postdocs, and early-career industry researchers—can present their research and interact with UMD’s machine learning community.

“By promoting machine learning research through this program, we not only advance the field, but also inspire a diverse group of students to join us,” says Haizhao Yang, an associate professor of mathematics and member of the machine learning center who chaired this year’s program.

From the 85 applicants that applied in 2024, six winners were selected to present their work. Five traveled to College Park for the December workshop, and one presented their work virtually.

The workshop, held in the Brendan Iribe Center for Computer Science and Engineering, commenced with a meet-and-greet breakfast and opening remarks from Tom Goldstein, professor of computer science and director of the UMD Center for Machine Learning.

Both Goldstein and Yang have appointments in the University of Maryland Institute for Advanced Computer Studies (UMIACS), which helped coordinate the event.

Ying Fan, a doctoral candidate at the University of Wisconsin–Madison, was the first Rising Star to present their work after the opening remarks. Fan’s work focuses on sequential learning problems, including reinforcement learning and human feedback for diffusion models and large language models.

“What struck me most about the workshop was the collaborative atmosphere and the opportunity to engage in meaningful discussions about the future of AI,” she says. “The mentorship sessions provided invaluable insights that will shape my research approach as I advance in my career.”

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