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Serena has joined the third cohort of researchers whose mission is to understand the adoption of artificial intelligence across economies, industries, and communities.

The need is clear: AI is rapidly permeating journalism, increasing dependency on a few Big Tech companies who control much of the infrastructure underpinning commercial AI. Many in the industry have raised alarms about what this means for editorial independence, labor, and the long-term viability of journalism itself.

The Brown Systems research group has broken their own record with four accepted papers. One of these won both a Jay Lepreau Best Paper Award and a Best Artifact Award and another was a Best Paper Award Runner-Up.

His project aims to build new graph algorithms that work in the presence of length constraints and, in doing so, provide new algorithms for solving classic problems as fast as possible.

With a course offered this past spring semester, professors and students alike have begun grappling with the role automated AI agents have in teaching students the basics of software development.

"Most benchmarks for AI agents," the authors explain, "usually only ask one question, did the agent complete the task? And what they don't ask is whether the agent should have completed the task in the first place."

Brown CS is happy to announce that with the anticipated approval of Brown University’s Corporation and effective as of July 1, 2026, Srinath Sridhar has been promoted to Associate Professor with tenure.

Peihan is the John E. Savage Assistant Professor of Computer Science, Chen is the Eliot Horowitz Assistant Professor of Computer Science, and Suresh is the Dean's Professor of Data Science and Computer Science.

"These are our top students," says Director of Undergraduate Studies Kathi Fisler. "As a group, they've done superior work, but we were also very impressed with how generously they were able to give back to the department and their fellow students."

She'll study how people communicate goals to machines and design AI systems that can interpret imperfect instructions by reasoning about the intent behind them. Expected outcomes include safer decision-making technologies and new tools that help organizations deploy AI more effectively.