What do multiple sclerosis and algorithms have in common?
Seemingly very little: one is a complex neurologic disease, while the other is a mathematical framework that powers everything from GPS systems to artificial intelligence. But at Stony Brook University’s latest Provost’s Lecture Series event, both were presented as examples of the same essential pursuit: understanding what lies beneath the surface.
Stony Brook, NY, February 22, 2026 — Scroll through Instagram or X long enough, and you’ll see it — a reel insisting that “fruits are citrus, so you shouldn’t eat them with milk,” a thread warning “protein shakes wreck kidney function,” a carousel promising “this workout routine will fix your PCOS in 30 days.” Every third post seems to offer a health hack, often backed by a chart, a DOI link, and just enough scientific language to sound convincing.
But behind those posts is a tangle of dense scientific research that few people ever read.
Two Stony Brook University research initiatives were awarded seed funding through the SUNY Technology Accelerator Fund (TAF), which supports groundbreaking research opportunities and helps faculty inventors and scientists turn their research into market-ready technologies.
SUNY TAF targets critical research such as feasibility studies, prototyping and testing, which demonstrate that an idea or innovation has commercial potential. The goal is to accelerate time to market for these innovations and increase their market readiness for potential investors, strategic partners and customers.
Hochul wants lawmakers to appropriate $60 million this year towards four new quantum hubs across the state as a way to propel the industry forward. She wants another $100 million invested in Stony Brook University’s Quantum Research and Innovation Hub, set to open in 2029.
Researchers have long recognised that for artificial intelligence to truly collaborate with people, it must accurately anticipate human intentions. Peter Zeng, Weiling Li, and Amie Paige, from Stony Brook University, alongside Zhengxiang Wang, Panagiotis Kaliosis, Dimitris Samaras et al, investigated how Large Visual Language Models (LVLMs) establish ‘common ground’ during communication , a fundamental aspect of human interaction. Their new study, detailed in a referential communication experiment, reveals a significant limitation in LVLMs’ ability to interactively resolve ambiguous references, using a unique dataset of 356 human and machine dialogues.
Meet Manas Singh, a junior majoring in Computer Science with a specialization in Artificial Intelligence and Data Science. He is currently an undergraduate researcher in the Language Understanding and Reasoning (LUNR) Lab under Dr. Niranjan Balasubramanian.
