The University of Rochester has named Mujdat Cetin as its inaugural associate vice president and vice provost for artificial intelligence, a University-wide leadership role effective September 1. The appointment creates a single point of coordination for AI research, education, and governance across the institution.
Cetin, who also directs the university's Goergen Institute for Data Science and Artificial Intelligence, will split his time evenly between the new post and his current directorship. He will report jointly to the provost and the vice president for research, and will lead development of an AI strategy aligned with the university's 2030 strategic plan.
Scope of the role
In practice, the position carries three main responsibilities. Cetin will coordinate AI research across schools and departments, including collaboration with the Laboratory for Laser Energetics, University of Rochester Medicine, and the university's transdisciplinary centers. He will oversee AI curricula and degree programs as well as workforce development efforts tied to regional employers and the Empire AI initiative. And he will provide University-wide AI governance, with the institution's AI Council reporting to him.
"Artificial intelligence is rapidly changing how we teach, learn, conduct research, and engage with the world," said Nicole S. Sampson, provost and chief academic officer. "Mujdat brings the combination of scholarly expertise, collaborative leadership, and institutional perspective we need to bring together the extraordinary AI work and shape a next chapter for our University."
Cetin's research background spans computational signal and imaging sciences, with particular focus on machine learning for brain-computer interfaces and biomedical imaging. He previously held faculty positions at MIT, Sabanci University, Northeastern University, and Boston University. For those working in science and research, the appointment is part of a broader movement of research universities treating AI strategy as a dedicated leadership function rather than an add-on to IT or departmental work.
Research coordination priorities
Developing university-wide AI policy touches on one of the most complicated parts of academic AI adoption: balancing innovation with guardrails. Cetin will directly oversee the creation of policies and ethical standards, and will be charged with aligning those standards with federal requirements and broader national norms. The dual relationship to the provost and research vice president also signals a need to integrate AI strategy with faculty and lab resources rather than keeping it siloed.
"Rochester has enormous strengths in data science, AI, engineering, medicine, the arts, and the humanities," said Cetin in a university statement. "I look forward to building on these foundations to create a shared vision for AI that serves the whole University."
The appointment is part of the university's broader AI for Science & Research efforts, which reflect a widening trend of institutions formalizing faculty-led AI governance rather than an IT-driven approach.
Why this matters for science and research professionals
Positioning at Rochester suggests a broader outlook for research institutions: expect more deliberate boundaries around how AI is applied in labs, consortia, and proposals. For scientists and researchers, the key takeaway is to follow this pattern of AI leadership roles for a concrete reason - AI policies will increasingly dictate questions like which datasets can be processed on which infrastructure, what approval paths support AI-assisted workflow for lab staff, and how universities are represented in federal research proposals.
When these roles work well, they can accelerate procurement, simplify ethics approvals, and open up joint funding calls. When they do not, they can slow aspects of research that depend on data sharing and multidisciplinary collaboration. The University of Rochester's choice - assigning an established faculty researcher to AI leadership rather than an administrative executive - signals they are weighing academic credibility over process speed. That tradeoff is one that research professionals across other institutions should watch.
The AI for Education role also focuses on the more pragmatic side of AI curriculum work: developing certificates, degrees, and experiential offerings that meet the expectations of the workforce in AI-related roles.
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