AI Innovation Institute

Harsh Trivedi, a doctoral researcher at Stony Brook University, was recently invited to present his work at the White House. His project, AppWorld, promises to revolutionize how we automate daily tasks in our digital lives.

Stony Brook, NY, Oct 19, 2024 - Stony Brook University Professor Niranjan Balasubramanian and Ph.D. researcher Harsh Trivedi were invited to represent the university at the White House Office of Science and Technology Policy (OSTP) launch event to celebrate the first allocation of the NAIRR Pilot.

The Provost’s Spotlight Talks feature eminent visitors to the university as well as Stony Brook faculty members who have recently been recognized for outstanding contributions in their field.

Research project supported by NIH uses AI to predict how much trust individuals and communities have in U.S. counties.

Stony Brook, NY, Oct 12, 2024 - In the United States, public confidence in institutions has been declining over the past several decades. It is easy to imagine how these low levels of trust might lead to negative social, political, and economic outcomes, especially in the heat of the 2024 presidential elections. This is why measuring trust consistently and at scale is crucial.

Stony Brook University professor Robert J. Harrison, founding endowed director of the Institute for Advanced Computational Science (IACS), has been appointed interim executive director of New York State’s first-in-the-nation Empire AI Consortium.

Collaborative research to test method in opioid use disorder.

Eleven endowed Stony Brook University faculty members will be formally installed on Friday, October 25, at a ceremony held at the Charles B. Wang Center Theater.

Novel research supported by NCI could lead to more specific predictive disease models.

A team of Stony Brook University researchers — led by two scientists in the Department of Biomedical Informatics in the Renaissance School of Medicine (RSOM) and College of Engineering and Applied Sciences (CEAS) — is developing a new way to analyze breast cancer imaging that incorporates mathematical modeling and deep learning. The approach will be much more interpretable and robust compared to previous methods. Their goal is to improve disease diagnosis and chart a treatment plan specific to the biomarker imaging and modeling findings.