Please join the Department of Human Centered Design & Engineering for the 2016 HCDE Seminar Series. Full series at hcde.uw.edu/seminar-series.
Extraordinary advances in our ability to acquire and generate data are transforming the fundamental nature of discovery across domains. Much of the research in data science has focused on automated methods of analyzing data such as machine learning and new database techniques. Less attention has been directed to the human aspects of data science, including how to build interactive tools that maximize creativity and human insight, and the ethics and societal factors involved in the next generation of data science discoveries. In this talk, Dr. Aragon will argue for the importance of a human centered approach to data science as necessary for the success of 21st century discovery. Further, she attests that we need to go beyond well-designed user interfaces for data science software tools to consider the entire ecosystem of software development and use: we need to study people interacting with technology as socio-technical systems, where both technical and social approaches are interwoven. Aragon will discuss promising research in this area, introduce the new Master's Degree in Data Science at UW, and speculate upon future directions for data science.
About Cecilia Aragon
Dr. Cecilia Aragon is a 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. She has authored or co-authored over 80 peer-reviewed publications and over 100 other publications in the areas of HCI, CSCW, data science, visual analytics, machine learning, and astrophysics. In 2008, she received the Presidential Early Career Award for Scientists and Engineers (PECASE), the highest honor bestowed by the US government on outstanding scientists in the early stages of their careers, for her work in collaborative data-intensive science.