Being Human in a Big Data World: Human-Centered Data ScienceCECILIA ARAGON, HCDE ASSOCIATE PROFESSOR
DECEMBER 9, 2015
241 MARY GATES HALL
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.
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.