Graduate Students

05/20/2024 - The National Science Foundation (NSF) has announced the recipients of its prestigious Graduate Research Fellowships (NSF GRFP) for 2024, and students from the Department of Computer Science at The University of Texas at Austin's College of Natural Sciences (CNS) have been prominently recognized. This year, four Computer Science students were honored with fellowships or honorable mentions, highlighting their outstanding contributions and potential in various cutting-edge research areas.

02/01/2024 - 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.

11/15/2023 - Academia can be opaque from the outside to folks who wonder what being a professor is like. This can be a hurdle for aspiring graduate students, who are interested in the role, but are anxious about all the different things that are required as a professor. Vijay Chidambaram, Professor of Computer Science at UT Austin, has authored a book, "The CS Assistant Professor Handbook: Essential know-how for new and aspiring computer science professors", to tackle this challenge.

11/08/2023 - In mathematical optimization, a new approach is emerging, promising to transform how we tackle intricate challenges across various domains. Consider the complexity of bilevel optimization, a problem that has confounded experts in machine learning, engineering, and other fields. Recent advances are providing new insights into this intricate landscape, presenting a streamlined technique that has the potential to significantly enhance our ability to navigate these complex problems.

11/02/2023 - Smriti Singh spends a lot of time on social media. But, not in the way most students do.

09/27/2023 - The work of researchers from The University of Texas at Austin’s Department of Computer Science in crash consistency has yielded a breakthrough innovation—the Chipmunk system. At its core, Chipmunk zeroes in on a crucial mission—meticulously testing file systems to identify and tackle crash consistency bugs that can significantly impact data integrity and system reliability. The UT Austin team has produced a promising solution that could pave the way for a new era in data storage and stability.

05/01/2023 - The work relies in part on a transformer model, similar to the ones that power ChatGPTA new artificial intelligence system called a semantic decoder can translate a person’s brain activity — while listening to a story or silently imagining telling a story — into a continuous stream of text. The system developed by researchers at The University of Texas at Austin might help people who are mentally conscious yet unable to physically speak, such as those debilitated by strokes, to communicate intelligibly again.

04/21/2023 - UT Computer Science Ph.D. Garrett Bingham’s research under Professor Risto Miikkulainen in smart automated machine learning has made significant steps toward more efficient neural network systems.

05/24/2022 - As chips become larger and more complex, their routing algorithms must also be improved and optimized to face the challenge. Recent UT Computer Science PhD graduate Michael Jiayuan He’s research in computer chip design has made a notable step forward towards more efficient and successful chip design in the academic arena.

02/10/2022 - In his recent paper, “Faster Coherent Quantum Algorithms for Faster Phase, Energy, and Amplitude Estimation”, UTCS PhD graduate Patrick Rall puts forth novel quantum algorithms for estimating important fundamental qualities of our complex world. Patrick’s approach simplifies the necessary computations compared to the current standard method. Estimation of these properties has applications in condensed matter physics and quantum chemistry as well as machine learning and finance.