Being Human in a Big Data World: Human-Centered Data ScienceCECILIA ARAGON, HCDE ASSOCIATE PROFESSOR
DECEMBER 9, 2015
4:30—5:20 P.M.
241 MARY GATES HALL
4:30—5:20 P.M.
241 MARY GATES HALL
Small
scale, qualitative approaches to data collection and analysis offer
researchers the opportunity to obtain very rich, deep insights about
very specific phenomena - often in a very bounded or limited context.
Such studies often face challenges related to generalization, extension,
verification, and validation. On the other hand, large scale,
quantitative approaches to data collection and analysis offer
researchers broad assemblages of data, but such data is often much more
shallow - missing the rich detail associated with deep study.
But
what happens as qualitative data sets grow ever larger? With the ease
of collecting qualitative data such as text and multimedia photos and
videos, such data sets are becoming an increasing challenge to analyze
with the same level of detail and depth. How do we preserve the richness
so well associated with traditional qualitative techniques in a world
of such Big Data? How can we be sure not to lose the compelling and
inspiring stories of individuals in the sea of aggregated data at scale?
There
are clear advantages of each perspective - one can choose methods and
techniques which facilitate deep, but narrow analysis, or one can be
broad, but shallow. In this talk, Cecilia Aragon will discuss and
explore some of the particular sets of problems and challenges
sociotechnical researchers face with regards to this small-data versus
big-data tension, and seek ways of overcoming (or at least identifying
potential solutions to) these problems and addressing the challenges.
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About Cecilia Aragon
Dr.
Cecilia Aragon is an associate professor in the department of Human
Centered Design & Engineering and a Senior Data Science Fellow at
the eScience Institute at the University of Washington. She directs the
Human-Centered Data Science Lab. Previously, she was a data scientist at
Lawrence Berkeley National Laboratory for six years, after earning her
Ph.D. in Computer Science from UC Berkeley in 2004. She earned her B.S.
in mathematics from the California Institute of Technology.
Her
research focuses on human-centered data science, an emerging field at
the intersection of human-computer interaction (HCI), computer-supported
cooperative work (CSCW), and the statistical and computational
techniques of data science. She and her students develop collaborative
visual analytics tools to facilitate data science, and study current
scientific practice around large and complex data sets. Her research
interests span the areas of HCI, CSCW, data science, visual analytics,
machine learning, and astrophysics. In 2009, she received the
Presidential Early Career Award for Scientists and Engineers for her
work in collaborative data-intensive science.
