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Data Science

The Task Force:

Bean, Rachel Astronomy
Brachman, Ron Jacobs Technion-Cornell Institute
Clark, Andrew Molecular Biology and Genetics
Cornwell, Ben Sociology
Easley, David Economics
Enns, Peter Government
Gore, Michael Integrative Plant Sciences
Hooker, Giles Biological Statistics and Computational Biology
Joachims, Thorsten Computer Science and Information Science
Kleinberg, Jon Computer Science and Information Science
Liaukonyte, Jura Applied Economics and Management
Liivak, Oskar Law
Linster, Christiane Neurobiology and Behavior
Osofsky, Steven Population Medicine and Diagnostic Sciences
Pathak, Jyotishman Weill Department of Medicine
Shmoys, David Operations Research and Information Engineering
Wagner, Aaron Electrical and Computer Engineering
Weinstein, Harel Weill Department of Medicine
Wells, Marty Biological Statistics and Computational Biology
Wittich, Peter Physics

The Charge:

  1. What organization, structure, leadership or mechanisms will create a robust and inclusive academic research environment for data science that facilitates recruitment and success of the most promising scholars in the emerging discipline, effectively connects faculty working across the spectrum of discovery through application in data science, enhances the potential for external funding, and provides an effective platform for educational efforts? For example, should we have a Graduate Field of Data Science? Should there be a Data Science Institute? If so, how should it be funded? Should we consider changes in our current department structure?
  2.  Cornell has a number of units that already engage with and support data science across the university, including the Cornell Center for Advanced Computing (CAC), the Bioinformatics Facility within the Institute of Biotechnology, the Research Data Management Service Group (RDMSG), the Cornell Statistical Consulting Unit (CSCU), the Cornell Institute for Social and Economic Research (CISER), and the Survey Research Institute (SRI), among others. In this context, do we have the right organizational structure and capabilities to achieve the goals set forth above? Are support systems appropriate for the research communities seeking to access data analysis?
  3. How can we advance data science educational programs at the undergraduate, masters, and Ph.D. levels in the most effective way?
  4. Should Cornell focus on building specific areas of data science in which we can most easily achieve a competitive advantage relative to our peers due to existing strengths at the university? If so, which areas are most promising?
  5. Are there ways to organize our recruitment efforts so as to enhance the interactions and collaborations of faculty that are recruited to Cornell in the discovery and application domains?