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Research

Investigating How to Make Robots Better Team Members

surgical team in operating room monitoring patient stats

07/17/2020 - Imagine that you are a robot in a hospital: composed of bolts and bits, running on code, and surrounded by humans. It’s your first day on the job, and your task is to help your new human teammates—the hospital’s employees—do their job more effectively and efficiently. Mainly, you’re fetching things. You’ve never met the employees before, and don’t know how they handle their tasks. How do you know when to ask for instructions? At what point does asking too many questions become disruptive?

Pangolin: An Efficient and Flexible Graph Mining System on CPU and GPU

Illustration of a pangolin with line and bar graphs

06/11/2020 - The datasets used by many software applications can be represented as graphs, defined by sets of vertices and edges. These graphs are rich with useful information, and can be used to determine patterns and relationships among the stored data. This process of discovering relevant patterns from graphs is called Graph Pattern Mining (GPM). A team of Texas Computer Science (TXCS) researchers advised by Dr. Keshav Pingali has done groundbreaking work to make GPM programs more efficient and accessible.

TXCS Researchers Design Evolutionary Algorithms for Neural Networks

Plot of the activation functions the researchers discovered

05/28/2020 - Artificial Intelligence (AI) is a rapidly evolving field, with advancements occurring every day. While the idea of an artificial intelligence system may conjure images of an autonomous machine that rattles out facts like a hi-tech encyclopedia, complex AI exists only because a countless number of talented individuals dedicate their time toward refining these systems.

New Partnership Aims to Demystify Artificial Intelligence “Black Boxes”

03/25/2020 - The promise of artificial intelligence to solve problems in drug design, discover how babies learn language, and make progress in many other areas has been stymied by the inability of humans to understand what's going on inside AI systems. Researchers at six universities, including The University of Texas at Austin, are launching a partnership aimed at turning these AI "black boxes" into human-interpretable computer code, allowing them to solve hitherto unsolvable problems.

TXCS Researchers Win Supercomputing Best Paper Prize

Robert van de Geijn, Lee Killough, and Tze Meng Low accepting the award at PP20 in Seattle, Washington

03/04/2020 - The paper titled “The BLIS Framework: Experiments in Portability” recently received the 2020 SIAM Activity Group on Supercomputing Best Paper Prize. Among the authors of this paper are TXCS professor Dr.

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