Rising Stars Workshop Engages Machine Learning Community

In a conscious effort to improve inclusion and diversity, the University of Maryland Center for Machine Learning hosts an annual program aimed at supporting graduate students, postdocs, and junior industry researchers from underrepresented groups who are active in computer science and machine learning.

Now in its fifth year, the Rising Stars in Machine Learning program provides a $500 honorarium and all-expenses-paid trip to College Park, where selected scholars present their research and engage with UMD’s machine learning community.

“This initiative is designed to position these emerging researchers at the forefront of their field, giving them access to valuable networking, mentorship, and professional development opportunities,” says Soheil Feizi, an associate professor of computer science who leads the program.

In prior years, the winning researchers would visit the UMD campus separately as part of the center’s distinguished seminar series. But this year, the center decided to hold a two-day workshop—joining all the program winners with UMD’s machine learning faculty and graduate students—with the goal of fostering a broader dialogue and a more diverse and inclusive community.

Eight winners were selected for this year’s program out of more than 85 applicants. Six traveled to College Park for the November workshop, and two presented their work virtually.

“This workshop surpassed my imagination in its perfection,” says Laixi Shi, a postdoctoral fellow at CalTech who specializes in reinforcement learning. “The experience has been immensely valuable to me, from witnessing the diverse research visions of fellow participants, to having in-depth conversations with University of Maryland professors and students and enjoying the beautiful views from the rooftop of the Iribe Center.”

The workshop kicked off with breakfast and opening remarks from Feizi, followed by additional commentary from Amitabh Varshney, professor of computer science and dean of the College of Computer, Mathematical, and Natural Sciences.

In addition to academic presentations from the Rising Stars participants, the workshop included a panel on career development, one-on-one meetings, and closing remarks from Matthias Zwicker, professor and chair of computer science.

“I had great conversations with the faculty and other Rising Stars, which made it a fantastic learning experience for both my research and my career,” says Han Shao, a doctoral candidate at the Toyota Technological Institute at Chicago who studies machine learning theory.

Three of the other Rising Stars are doctoral candidates who specialize in natural language processing—Wanrong Zhu from the University of California at Santa Barbara; Weiji Shi from the University of Washington; and Zhijing Jin from the Max Planck Institute and Federal Institute of Technology Zürich.

The rest of this year’s cohort included doctoral candidates Megha Srivastava from Stanford University who studies challenges in human-AI interaction; Sanae Lotfi of New York University whose research is focused on deep learning; and Yutong Bai from Johns Hopkins University who studies different ways to advance AI.

Financial support for this year’s program was provided by the UMD Center for Machine Learning, the Department of Computer Science, the University of Maryland Institute for Advanced Computer Studies, and the Institute for Trustworthy AI in Law & Society.

“It's moments like these that highlight what a powerhouse UMD is in artificial intelligence and machine learning, and how we've built such a strong, close-knit community,” says Furong Huang, an assistant professor of computer science and member of the Rising Stars program committee. “Being part of this community and supporting and witnessing the growth of our next generation of stars in artificial intelligence and machine learning, is something truly special.”

—Story by Maria Herd, UMIACS communications group

This article was published by the University of Maryland Institute for Advanced Computer Studies.

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