News Category: research

Microsoft Research Partners with UT Austin, Texas iSchool for Microsoft Ability Initiative

March 29, 2019

Despite significant developments in the world of automated image captioning, current image captioning approaches are not well-aligned with the needs of people with visual impairments. People who are blind or with low vision share a unique and real challenge –their visual impairment exposes them to a time-consuming, and sometimes, impossible task of learning what content is present in an image without visual assistance. As such, these communities often seek a visual assistant to describe photos they take themselves or find online. 

King Davis to Receive Benjamin Rush Award for Central State Hospital Archives Project

Feb. 28, 2019

Texas iSchool Colleague and Research Professor King Davis was recently selected for the Benjamin Rush Award from the American Psychiatric Association for, “The Central State Hospital Archives Project.” Known as the “Father” of American psychiatry, Rush is celebrated for his multiple contri

The Future of Search Engines

Aug. 31, 2018

Search engines have changed the world. They put vast amounts of information at our fingertips. But search engines have their flaws, says iSchool Associate Professor Matthew Lease. Search results are often not as “smart” as we’d like them to be, lacking a true understanding of language and human logic. They can also replicate and deepen the biases embedded in our searches, rather than bringing us new information or insight.

The University of Texas at Austin Awarded Grant from Chan Zuckerberg Initiative to Support Human Cell Atlas

July 13, 2018

Chan Zuckerberg Initiative DAF (CZI), a donor-advised fund of Silicon Valley Community Foundation, recently awarded a grant to the University of Texas at Austin to support the work of Assistant Professor Danna Gurari. The project titled, “Video Analysis: Efficiently Tracking and Detecting Life Cycle Phase Transitions for Live Cells,” aims to design frameworks and systems that close the gap between computer vision (CV) algorithm and human performance for analyzing living cells observed in videos.