Jian Li and Akshat Dave Among Faculty Awarded Grants From AI Innovation Institute

Ten interdisciplinary projects have received funds from Stony Brook University's AI Small Grant Program.

Selected from a pool of 63 applications, these research initiatives will advance integrated AI innovation and strategic diffusion of AI technology across Stony Brook's campus, New York State and the world.

Lav Varshney, Della Pietra Infinity Professor and inaugural director of the AI Innovation Institute (AI3), said, "It is important to integrate the advances we make in foundational AI technology with its use in important settings like the treatment of epilepsy and finding early biomarkers for mental health disorders. The selected projects are very much pioneering in this way, sitting at the hard intersection of innovation and diffusion. Some projects are further developing datasets and benchmark tasks that can drive whole ecosystems of research effort towards societally important problems. I'm also super excited by projects that are leveraging AI to drive novel sensing of biological, quantum, and cosmological phenomena and delving into the deep history of mammalian evolution."

The grant sought projects in AI, its applications, and its ecosystems along three distinct tracks. Projects selected for "Innovation in AI" focus on new advances in algorithms, architectures, mathematical foundations, physical foundations or ethical foundations of AI. Those aimed at "Diffusion of AI" conduct research that facilitates the diffusion of AI into an industrial, societal or scholarly sector, largely focused on using AI in novel settings.

Projects in the third category seek to develop "Datasets and Benchmark Tasks" appropriate for advancing AI and for respecting their disciplinary core, whether that's business, engineering, health sciences, humanities, journalism, marine sciences, physical sciences, social sciences or other fields.

1. Regression-Gated Continual Alignment of Tool-Using LLM Agents

Principal Investigator:
Jian Li, associate professor in the Department of Applied Mathematics & Statistics, & affiliated faculty in the Department of Computer Science, CEAS

The Project:
Large language models behind AI services and agents are updated frequently to meet shifting user needs, new safety policies, and revised software interfaces. Each update carries a risk: improving one behaviour can break another that worked before, so an assistant might begin failing a safety check it once passed. Current alignment methods offer no guarantee against these regressions, thus eroding trust.
This project will develop Regression-Gated Continual Alignment, which reframes alignment as continual learning under constraint. The framework limits how much a model may change at once and applies explicit regression gates - a compact suite of safety, policy, and tool-validity tests an update must pass before release. Deliverables include formal metrics for regression risk, updated algorithms with mathematical guarantees, and a working prototype.

2. Towards Automated Non-invasive Consciousness Monitoring for Neurocritical Care

Principal Investigator:
Akshat Dave, assistant professor in the Department of Computer Science, CEAS

Co-Principal Investigator:
Ulas Sunar, SUNY Empire Innovation professor in the Department of Biomedical Engineering, CEAS

The Project:
After traumatic brain injury, around 40% of patients diagnosed vegetative are, in fact, conscious - a condition invisible to behavioural assessment. The standard assessment occupies a trained examiner only for up to half an hour, capturing a snapshot and unable to detect hidden awareness. Such misdiagnosis can lead to life support being withdrawn prematurely, making objective monitoring an urgent unmet need. This project will combine EEG with diffuse correlation spectroscopy which uses near-infrared light to measure deep cerebral blood flow, continuously recording brain-injured intensive care patients across five states of consciousness. An AI model will fuse these streams across timescales from milliseconds to days. All data, code, and benchmarks will be released publicly, the first such resource of its kind.

Launched in February 2026, the AI3 Small Grant Program was developed and funded by AI3. The Office for Research and Innovation supported the application process and oversaw the committee that reviewed and selected winning proposals. The grant distributed more than $450,000 across the ten projects. AI3 will also connect researchers with graduate consultants and experts at the Stony Brook Libraries to help them better execute their projects.

For details on the remaining selected projects, read the full story on the AI3 website.