Data Driven Science: SIGKDD Panel

The panel session 'Data Driven Science' discusses application and use of knowledge discovery, machine learning and data analytics in science disciplines; in natural, physical, medical and social science; from physics to geology, and from neuroscience to population health. Knowledge discovery methods are finding broad application in all areas of scientific endeavor, to explore experimental data, to discover new models, to propose new scientific theories and ideas. In addition, the availability of ever larger scientific data sets is driving a new data-driven paradigm for modeling of complex phenomena in physical, natural and social sciences. The purpose of this panel is to bring together users of knowledge discovery, machine learning and data analytics methods across the science disciplines, to understand what tools and methods are proving effective in areas such as data exploration and modeling, to uncover common problems that can be addressed in the KDD community, and to explore the emerging data-driven paradigm in science.