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Faculty News
A collaborative paper led by researchers at UMass Amherst, with contributions from Assistant Professor Longfei Shangguan and the University of Maryland, Baltimore County, recently received a Distinguished Paper Award at UbiComp 2026.
Fourteen SCI faculty members and six Intelligent Systems Program (ISP) faculty members ranked among Research.com’s 12th Edition of Best Computer Scientists in the U.S. and World’s Best Computer Scientists.
Dr. Xulong Tang, an associate professor in the Department of Computer Science, is participating in a five-year $5 million NSF ExpandQISE Track 2 Grant. The project, titled “ExpandQISE: URI-PQI Collaboration - Application of Quantum Fundamentals to Advance Research and Workforce Development,” is led by the University of Rhode Island in collaboration with the Pittsburgh Quantum Institute, Carnegie Mellon University, and the University of Pittsburgh.
Student News
Senior computer science Student Griffin J. Hurt has been named a 2024 Graduate Research Fellowship Program (GRFP) scholar.
Two students from the School of Computing and Information (SCI) Department of Computer Science have been recognized for their undergraduate research by the Computing Research Association (CRA)!
Last month, 33 students (18 in person and 15 virtually) from the School of Computing and Information (SCI) attended the Grace Hopper Conference (GHC) in Orlando, FL.
Colloquium Talks
Software development is undergoing a major shift from human coding to AI generation. While the rise of “vibe coding” promises massive productivity gains, it introduces severe security risks. This talk provides a clear demonstration of AI coding risks, practical approaches to train secure agents, and insights into the future of autonomous security defense.
Software development is shaped by constant disruption—from open-source dependency abandonment to generative AI (GenAI) tools fundamentally reshaping how code is written. In this talk, I'll present two lines of work, each examining a different disruption. Finally, I'll outline my research vision for helping developers anticipate and navigate the disruptions of tomorrow—from building AI-powered tools for software supply chain resilience to studying how GenAI is reshaping development team practices and processes.
Modern machine learning remains dependent on a paradigm in which considerable up-front effort produces models that are monolithic, opaque, and costly to change once trained. In this talk, I will discuss three active lines of research from the AI Division at the Software Engineering Institute that aim to make ML models easier to maintain.