Search Results for "-≒구글찌라시╊@hhu9999╊개인장1페이지홍보#전문구글상위노출㎂최저가섹밤0찌라시-광고부달광고∽.픠.-Fant-ikke-noe-på-det-søket.-.lki"

Congratulations to Sanghani Center’s 2024 Spring Graduates

Virginia Tech’s week of commencement ceremonies is underway! The Graduate School Commencement ceremony was held Wednesday, May 8; the main ceremony is being held today, Friday, May 10; and the Washington, D.C. area ceremony will be held on Sunday, May 12.   “Graduation is always a bittersweet time for faculty as we applaud our students’ accomplishments,” […]

Layne Watson honored with emeritus status

Layne Watson, professor of computer science in the College of Engineering at Virginia Tech, has been conferred the title of professor emeritus by the Virginia Tech Board of Visitors. Watson is also a core faculty member at the Sanghani Center for Artificial Intelligence and Data Analytics. The emeritus title may be conferred on retired faculty members who are specially recommended […]

Class of 2024: Andreea Sistrunk graduates with a Ph.D., a life lesson, and a motto to live by

Andreea Sistrunk’s motto, “A best solution to everything is up to us to uncover,” evolved on her path to earning a Ph.D. in computer science at Virginia Tech’s Northern Virginia campus. “In the beginning, I found myself overwhelmed and at times discouraged by how fast technology is advancing,” she said. “As hard as I was […]

Virginia Tech to exhibit at the AI Expo for National Competitiveness

Virginia Tech will exhibit at the first AI Expo for National Competitiveness at the Walter E. Washington Convention Center in Washington, D.C., on May 7-8. Chaired by former chief executive officer of Google Eric Schmidt, the expo serves as a forum for industry, government, and academic research entities to exhibit some of the latest technological breakthroughs in […]

Jaganmohan Chandrasekaran

Jaganmohan Chandrasekaran is a research assistant professor at the Sanghani Center. His research interest is at the intersection of software engineering and artificial intelligence, focusing on addressing the engineering challenges in building, deploying, and maintaining AI‐enabled software systems. His research aims to advance the state of the art in evaluating AI systems by developing methods, […]

Sanghani Center and CAIA cultivate transdisciplinary research in agriculture, AI, and data analytics

A new initiative between the Center for Advanced Innovation in Agriculture (CAIA) and the Sanghani Center for Artificial Intelligence and Data Analytics has launched a Graduate Research Assistantship Program that provides scholarships for exceptional Ph.D. students conducting research that generates agricultural solutions enabled by artificial intelligence and data analytics.  The initiative builds upon a consistent collaboration between the two […]

Matthew Zheng

Matthew Zheng is a master’s degree student in the Department of Computer Science. He is advised by Pinar Yanardag. Zheng’s research is primarily in generative modeling in computer vision and currently focused on text-to-image and text-to-video diffusion models.

Six Virginia Tech faculty elected AAAS Fellows

The American Association for the Advancement of Science (AAAS), one of the world’s largest general scientific societies and publisher of the Science family of journals, has named its 2023 class of fellows. Six Virginia Tech researchers are named among the latest class of 502 scientists, engineers, and innovators. The newly elected AAAS Fellows represent the […]

Octo CEO to deliver 2024 commencement address

Mehul Sanghani, chief executive officer and founder of Octo, will deliver the keynote address at Virginia Tech’s University Commencement ceremony on Friday, May 10. Octo is a technology firm focused on solving national security’s most complex problems. In the last decade, Sanghani built and grew the company around the foundational belief of meeting, exceeding, and […]

Connor Dunlop

Connor Dunlop is a master’s degree student in the Department of Computer Science. His advisor is Pinar Yanardag. Dunlop’s research focuses on interpretability and fairness in generative models with an emphasis on large language models.