As data volumes continue to grow across scientific research, so does the need for advanced computing infrastructure to manage, analyze, and connect information from myriad sources. Now, a new high performance data facility aims to help researchers unlock discoveries faster by combining AI (artificial intelligence), high-performance computing, and large-scale data management. The facility is supported through partnerships that include Virginia Tech and the U.S. Dept. of Energy. The high-performance data facility will serve as a national hub for scientific data, enabling researchers to access and analyze information from laboratories, experiments, and simulations across the country. By bringing together computing power, networking, and advanced data tools, the facility is designed to accelerate research in a wide range of scientific disciplines. Here is how this can support scientific research: Looking to the future, the facility is expected to strengthen the nation’s scientific data infrastructure and create new opportunities for innovation in fields ranging from physics and energy research to biology and environmental science. As research generates increasingly complex datasets, investments in advanced data and AI capabilities will play an important role in supporting the next generation of scientific…

As AI (artificial intelligence) and LLMs (large language models) continue to transform software development, universities are investing in research that explores new ways to help developers build, maintain, and secure increasingly complex software systems. As one example, Virginia Tech computer science researcher Muhammad Ali Gulzar recently received a National Science Foundation CAREER Award to advance research on how large language models understand and interpret software code. His project, titled “Foundations of Semantic Code Understanding by Large Language Models for Software Maintenance,” seeks to improve the ability of AI systems to reason about code in ways that more closely align with human developers. What makes this so innovative is the focus on semantic code understanding—the ability of AI systems to comprehend the meaning and intent behind software rather than simply recognizing patterns in code. By developing foundational methods for evaluating and improving how LLMs interpret software, the research aims to make AI-assisted debugging, testing, and software maintenance more accurate and trustworthy. Here is how this can help: Looking to the future, we will continue to see growing investments in AI-driven software engineering and intelligent development tools. As organizations increasingly rely on large-scale software systems and AI-assisted programming, research focused on improving the accuracy, transparency, and trustworthiness of large language models will play a critical role in shaping the future of digital…