News
The newsletter for alumni and friends of Purdue CS for June 2024. At Purdue Computer Science, we advance the profession through research and our graduates solve complex and challenging problems in many fields.
Purdue CS graphics and vision faculty presented 13 papers at CVPR 2024
Purdue CS computer graphics and computer vision faculty presented thirteen papers at the 2043 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) is the No. 1 conference in Engineering & Computer Science per Google Scholar.
Finding ways to safeguard computer systems, networks and the data they hold is no easy task. And it’s growing ever more challenging, especially with the rise of cybersecurity threats, ransomware and other attacks. Purdue continues to equip the next generation of cybersecurity experts and industry leaders, offering one of the nation’s most storied, comprehensive and highly ranked undergraduate programs in cybersecurity.
Purdue's graduate programs continued their elevation in latest U.S. News & World Report rankings
“Purdue graduate students and faculty in master’s, doctoral and professional degree programs are among the best in the country,” Purdue President Mung Chiang said. “The latest graduate and research rankings reflect the success of our students and colleagues across multiple colleges as we continue to support scholarly excellence at scale.”
Can science make it too costly for hackers to attempt to steal information?
Jeremiah Blocki, an associate professor of computer science in Purdue’s College of Science, applies his work with passwords and secure systems to stem the ongoing tide of hackers by finding new and better ways to store information as securely as possible. Researchers take several angles to explore password security beyond the logon screen of your favorite website.
Assistant Professor Tianyi Zhang won a National Science Foundation (NSF) CAREER award for his proposed work titled, “Regularizing Large Language Models for Safe and Reliable Program Generation." His project aims to address the challenges associated with using Large Language Models (LLMs) for program generation and develop approaches to enhance the correctness, safety, and robustness of LLM-generated code.