Transdisciplinary Foundations of Data Science

The goal of this workshop is to explore transdisciplinary foundations of data science and to explore the creation of a research community to continue discussion beyond the workshop. Background Despite rapid growth in data science applications, there is inadequate progress on data science foundations. Lack of a strong foundation and understanding of scientific issues (e.g., generalizability, reproducibility, computability, and prediction error bounds) makes it difficult for practitioners to develop reliable models from data and for the public to trust the models derived from big data. For example, despite access to tremendous volumes of data and computational resources as well as early successes, the Google flu trend analysis led to major disappointments, which are described in the recent articles in Nature, Science and PLOS. In addition, data science applications are facing public criticism…


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