Research

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.

04/11/2023 - AUSTIN, Texas — The University of Texas at Austin and Amazon are launching a science and engineering research partnership to enhance understanding in a variety of areas, including video streaming, search and information retrieval and robotics.

03/03/2023 - A methodology developed by UT professors will allow the cost of verifying computations to be reduced by batching many separate arguments together. Brent Waters, a computer science professor and a co-author of the paper, was inspired to find a more efficient way to verify computations by refining techniques that had already come out over a decade ago.

12/14/2022 - For decades, natural language processing (NLP) has provided methods for computers to understand language in a way that mimics humans. Since they are built on transformers, complex neural network layers, these large language models' decision making processes are usually incomprehensible to humans and require large amounts of data to be trained properly. In the past, researchers have tried to remedy this by having models explain their decisions by providing rationales, short excerpts of data that contributed most to the label.

10/20/2022 - Autonomous robots will soon rove the buildings and streets of The University of Texas at Austin campus. But unlike other commercial delivery services, this fleet of robots will help researchers understand and improve the experience of pedestrians who encounter them.

09/23/2022 - As the technological world advances, it has become increasingly difficult for the speed of computers to improve. UT Computer Science Professor Dr. Chris Rossbach's research in field-programmable gate array (FPGA) virtualization has made significant strides in the development of a more efficient computing infrastructure.

08/05/2022 - The University of Texas at Austin and the MITRE Corporation, a nonprofit dedicated to solving problems for a safer world, have formed a partnership that includes accelerating innovative ethical artificial intelligence (AI) research currently underway by interdisciplinary teams of researchers who are part of UT Austin's Good Systems research grand challenge.