The grant will enable researchers to develop next-generation cryptographic protocols to protect sensitive communications and digital systems against future threats from quantum computers. The work aims to strengthen digital security as quantum technology advances, developing more efficient and scalable tools for safeguarding information and supporting complex security applications.

In a recent study published in Geophysical Research Letters, researchers at The University of Texas at Austin used remote sensing data and statistical methods to map a municipal aquifer in Nevada. Their goal is to create a lower-cost method to measure an aquifer’s permeability, how easily water flows through rock, that could aid groundwater management.

A new NSF-supported initiative will bring professional development and evidence-based teaching practices to faculty across SUNY, helping expand student success and retention in STEM fields. As part of a $2 million statewide collaboration, the project will provide training in learner-centered instruction, undergraduate research, AI-enabled teaching tools, and faculty mentoring networks. The effort aims to accelerate the adoption of proven teaching methods across SUNY and strengthen New York’s future STEM workforce.

Under a partnership, the Simons Foundation, Anthropic and OpenAI will commit $15 million in combined funding and resources. The foundation will manage and award the funding, including through an open call for proposals, to support workshops and initiatives that help researchers evaluate recent AI-assisted results, learn and use new tools, share perspectives and shape collective standards for AI use. Anthropic and OpenAI will also provide AI tools and computing resources to researchers supported by the Simons Foundation, extending beyond mathematics to include basic science.

Over the past two decades, research organizations worldwide have tended to become more alike in their research topics. At top institutions, new hires also fit particularly well into the existing research profile, a recent study by the Complexity Science Hub (CSH) finds.

- Professor Kyung Min Kim’s team in the Department of Materials Science and Engineering implemented both long-term and short-term memory in a single semiconductor device

- The team used gradual changes in electric current to develop encryption in which information can be read only after a set amount of time has passed

- The team also demonstrated a method in which the correct information appears only at a specific time, showing potential for use in cloud security