News

​Stony Brook University has received a nearly $3 million National Science Foundation Research Traineeship (NRT) award to support a five-year interdisciplinary graduate training program in data science and artificial intelligence. The initiative aims to cross-train PhD students from eight departments across the College of Arts and Sciences and the College of Engineering and Applied Sciences. Human-centered science students will gain computational skills and earn a graduate certificate in AI, while data science students will delve into social science methodologies and ethics, earning a certificate in human-centered sciences. The program, led by principal investigator Susan Brennan, seeks to address biases in data and AI systems by fostering collaboration between disciplines.

​The article "With post-pandemic AI, we've now stepped into the Age of Acceleration" from VentureBeat discusses how the COVID-19 pandemic has accelerated the adoption and integration of artificial intelligence across various industries. It highlights AI's expanding role in fields such as semiconductor design, software development, and even classical sculpting, emphasizing its capacity to augment human activities and significantly speed up production processes. The piece also addresses the potential for AI to drive a productivity boom, while noting concerns about job displacement and the need for thoughtful public policy to manage the transition

Stony Brook University and Brookhaven National Laboratory have jointly awarded $317,000 in seed funding to eight collaborative research teams. This initiative aims to foster interdisciplinary projects that leverage the strengths of both institutions, promoting innovation in various scientific fields. The selected teams will utilize the funds to advance their research endeavors, contributing to the broader mission of scientific discovery and technological advancement

A recent study highlights the transformative impact of machine learning on weather forecasting. By leveraging advanced algorithms, researchers have developed models that significantly enhance the accuracy and efficiency of weather predictions. These machine learning techniques outperform traditional numerical methods, offering faster forecasts with reduced computational demands. This advancement not only improves daily weather predictions but also holds promise for better preparedness against extreme weather events