​Nandita Kumari, a former graduate student at Stony Brook University, utilized machine learning to analyze lunar composition, aiming to identify resources like water and minerals for NASA's Artemis missions. Her research focused on quantifying rock compositions on the Moon, enhancing resource identification for future lunar exploration. Kumari's work contributes to sustainable space exploration by reducing reliance on Earth-supplied materials. She has since advanced her studies as a postdoctoral associate with the LunaSCOPE project at Brown University

​Dr. Jie Wei, a computer science professor at The City College of New York (CCNY), is leading a U.S. Air Force-funded project aimed at enhancing situational assessment and awareness (SAAW) through artificial intelligence and machine learning. The three-year, $299,000 grant supports the development of an efficient, robust, and explainable AI/ML system utilizing multi-modal sensing and deep learning techniques. Collaborating with Dr. Haibin Ling of SUNY Stony Brook, the project focuses on improving data and computing efficiency, ensuring robustness against noisy data and adversarial attacks, and providing transparent, interpretable, and certifiable decision-making processes. This initiative is critical for military operations, humanitarian assistance, and disaster response applications.

​Stony Brook University faculty members are addressing the environmental impact of data centers, which currently contribute approximately 2% of global greenhouse gas emissions—comparable to the entire airline industry. With a $1.5 million National Science Foundation (NSF) grant, the team is developing comprehensive metrics to assess sustainability costs, including energy usage and equipment lifecycle emissions. Their research also explores the use of energy-efficient edge nodes to reduce power consumption. This initiative aims to create verifiable, holistic sustainability measures to guide the design of greener computing services and inform policy decisions

Researchers at Emory University's Winship Cancer Institute have developed an AI-based biomarker that utilizes routine CT scans to predict how patients with non-small cell lung cancer (NSCLC) will respond to immunotherapy. The study, published in Science Advances, identifies "quantitative vessel tortuosity" (QVT)—the degree of twisting in tumor-associated blood vessels—as a key indicator. AI analysis of over 500 patient scans revealed that less twisted vasculature correlates with better responses to immune checkpoint inhibitors (ICIs). This non-invasive method could guide treatment decisions and reduce unnecessary costs, as ICIs can exceed $200,000 annually per patient. The research was led by Dr. Mohammadhadi Khorrami and Dr. Anant Madabhushi, with contributions from Case Western Reserve University and other institutions.