Professor Shyamal Mitra embodies a lifelong commitment to learning and resilience, transitioning from astronomy and physics to teaching computer science. Despite initial challenges and self-doubt when tasked with teaching a programming language he had yet to master, Mitra embraced the opportunity, learning alongside his students and fostering a culture of shared discovery.Read More
UT Computer Science is at the forefront of robotics innovation, aiming to propel the field forward. Highlighted in a recent article by KXAN, experts like Dr. Peter Stone and Justin Hart showcased their work, including advancements in generative AI, which is integral to tasks ranging from domestic chores to humanoid robot soccer, a part of the RoboCup Federation's ambitious goal of a robot team winning the World Cup by 2050.
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UT Computer Science Professor Kristen Grauman is among the eleven faculty members at The University of Texas at Austin who have been elected fellows of the American Association for the Advancement of Science (AAAS), including prominent figures in artificial intelligence from the university’s Department of Read More
On Sat, February 24, 2024, the UT Programming Team competed in the ICPC South Central USA Regional Competition. As a result, one team will advance to the ICPC North America Championship on Monday, May 27, 2024.Read More
The newly introduced Online Master's in Artificial Intelligence (MSAI) program at the University of Texas at Austin is strategically designed to meet the dynamic needs of the AI sector while placing a strong emphasis on ethical considerations. Throughout the program, students are immersed in challenging coursework, including a compulsory "Ethics in AI" course that underscores the importance of responsible AI utilization, incorporating assignments featuring AI tools such as ChatGPT.Read More
The University of Texas at Austin has welcomed its first cohort of students in the new, affordable, online artificial intelligence graduate degree program.
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Researchers from The University of Texas at Austin and McGill University delve into the mathematical intricacies of wire puzzle design. Focusing on geometrical aspects, they establish criteria for puzzle characteristics, emphasizing the importance of a challenging experience. The team introduces quantitative metrics to assess tunnel-bubble structures, demonstrating their effectiveness in distinguishing puzzles from non-puzzles. Their findings provide a foundation for an optimization model, shaping the future of wire puzzle design.
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