The 2018 Symposium on Data Science and Statistics (SDSS) will be held in honor of Edward J. Wegman, who has done seminal work in many areas within the interface of statistics and computing science—as well as data visualization—and has been a driving force in creating the SDSS and its predecessors.
SDSS is a continuation of the Symposium on the Interface of Computing Science and Statistics (“The Interface”). In 1967, a partnership between the Southern California Chapter of the American Statistical Association (ASA) and the Association for Computing Machinery (ACM) culminated in the first of 45 Interface symposia.
The symposia brought together computer scientists, statisticians, and mathematicians in addition to both established leaders and rising stars in transdisciplinary research. Illustrious keynote speakers have included Grace Wahba, John Tukey, John Nash, Sir David Cox, Bradley Efron, and Bill Cleveland, while the rich history and quality of the symposia are in large part due to the contributions of past program chairs such as Lynne Billard, David Scott, and Edward Wegman.
The new annual SDSS combines data science and statistical machine learning with the Interface Foundation of North America’s (IFNA’s) historical strengths in computational statistics, computing science, and data visualization. It stands on the shoulders of the above–mentioned giants and many others, and will continue the tradition of excellence by providing an opportunity for researchers and practitioners to share knowledge and establish new collaborations. SDSS is a partnership of the IFNA and ASA. IFNA is responsible for the program, and the ASA is responsible for operations.
Edward Wegman was a driving force behind early Interface symposia, spearheaded efforts to establish IFNA in 1987—which assumed responsibility for planning the symposia and publishing the proceedings—and drove the new partnership and expansion.
Call for paper
Call for paper description
Sessions will be centered on the following six topic areas:Data ScienceData VisualizationMachine LearningComputing ScienceComputational StatisticsApplications