Abstract: While artificial intelligence (AI) systems are transforming our daily lives, they still struggle to meet many real-world needs.  In this talk, I will present three ways our team is addressing misalignments between current AI paradigms and what is needed for real-world, vision-based applications.  This will include: (1) creating large-scale datasets that fill gaps in representing authentic cases; (2) designing efficient human-AI collaborations that compensate for AI’s shortcomings; and (3) developing compact models that deliver accurate performance with lower computational costs.  Together, these efforts highlight how we can design AI to be more practical, efficient, and ultimately empowering for people.

Bio: Dr. Danna Gurari is an Assistant Professor in the Computer Science Department at University of Colorado Boulder and Founding Director of the Image and Video Computing group. Her expertise is in computer vision, human computer interaction, accessibility, and (bio)medical data analysis, with an emphasis on interdisciplinary approaches. Gurari's research has resulted in over 60 peer-reviewed articles at top academic journals and conferences, with several papers earning awards at venues such as WACV, CHI, CSCW, ASIS&T, HCOMP, MICCAI, and AAPM. Over 30 mentees, spanning undergraduate students to postdoctoral fellows, contributed to this research before moving on to successful careers in industry and academia. Gurari's work is supported by diverse funding sources, including the National Science Foundation, Silicon Valley Community Foundation’s Chan Zuckerberg Initiative, Microsoft, Adobe, and Amazon.