At the University of Virginia, AI research is advancing rapidly across schools and disciplines. This collection of recent news stories highlights how UVA researchers are using AI to generate new knowledge, inform policy and practice, and address complex challenges across fields.
UVA professor David Danks has been named a 2026 AI2050 Senior Fellow by Schmidt Sciences, one of six researchers worldwide chosen for this honor. The three-year, $1 million fellowship will fund his interdisciplinary research into how AI systems, designed to reflect human values, may actually reshape those values over time.
A UVA expert explains the tech giant’s satellite test plan and whether putting AI systems in space could ease environmental pressures on Earth.
UVA computer scientist Chen Chen has earned a five-year, $600,000 NSF CAREER Award to build next-generation AI tools for epidemic intelligence. Combining case reports, wastewater data and mobility patterns, her research aims to improve disease forecasting, guide public health interventions, strengthen surveillance strategies, and eventually help communities understand local infection risks.
UVA cybersecurity expert Daniel Graham examines the privacy risks of Flock license plate readers, explaining how connected camera networks enable warrantless mass surveillance. He compares safeguards used for cell-site data under Carpenter v. United States, discusses real misuse cases, and outlines what technical fixes and policy changes could make these systems trustworthy.
Four University of Virginia School of Engineering faculty have received prestigious NSF CAREER Awards for research tackling major societal challenges through AI. Jason Bates, Chen Chen, Yu Meng, and Tianhao Wang are exploring nitrate conversion, disease forecasting, learning from imperfect data, and privacy-protecting synthetic datasets, respectively, each backed by roughly five-year, $600,000-plus grants.
Measuring pain in clinical settings is challenging, especially for patients who cannot self-report. Cori Espelien, a postdoctoral researcher at UVA’s Department of Public Health Sciences, is developing AI facial recognition software that automatically detects and classifies pain.
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CNN Business reporter David Goldman examined U.S.-China AI competition, drawing on research from UVA’s National Security Data and Policy Institute on China’s layered AI strategy. Dr. Phil Potter, NSDPI director, suggested both nations share concerns about uncontrolled AI and may benefit from cooperative frameworks that restrain private AI development while treating it as a national security priority.
AI search answers aren’t all the same kind of response. A University of Virginia librarian proposes sorting them into four types: factual, interpretive, constructive and strategic. Each requires different scrutiny, from verifying sources to weighing personal context, and recognizing which type you’re getting is key to deciding whether to trust it.
Anton Korinek, professor of economics at UVA’s College and Graduate School of Arts and Sciences and Darden School of Business, discusses the economic implications of artificial intelligence in this video featured in the 2026 Annual Research Report, produced by the Office of the Vice President of Research, explaining why AI deserves serious attention.
As AI adoption accelerates, alignment concerns aren’t limited to frontier labs like Anthropic and OpenAI. Darden faculty argue every organization deploying AI agents must address alignment internally, using stakeholder theory, broad employee involvement, clear purpose, aligned incentives and psychological safety to guide responsible, human-centered AI integration into workplace decisions now.
The National Security Commission on Emerging Biotechnology visited UVA for a day exploring the university’s work in biotechnology, AI, data science, and research security. Led by alumna Caitlin Frazer, the visit included tours, student discussions, and sessions highlighting UVA’s role in advancing national security priorities and innovation.
Two UVA School of Data Science faculty members have received $53,300 in Apple support for machine learning research at the School of Data Science.