Research and scholarship in the 21st century are being dramatically transformed by their interaction with data, in a vast array of new forms and at rapidly increasing levels of scale. This is a transformation that is gathering force across essentially all areas of study: it is visible, for example, in the profound effect of high-resolution molecular data and the ever growing genomic data in biology and medicine; in the ways in which digital traces of human behavior have enabled new styles of research in the social sciences; in the opportunities that computational analyses of large text collections have opened for scholarship in the humanities; and in the effect that data-rich approaches have had on central applications in engineering, agriculture, urban infrastructure; on driving breakthroughs in the physical sciences, and in many other areas. (From the December 2017 Data Science Task Force report)
The Charge
- 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?
- 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?
- How can we advance data science educational programs at the undergraduate, masters, and Ph.D. levels in the most effective way?
- 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?
- 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?
| Name | Field |
|---|---|
| Rachel Bean | Astronomy |
| Ron Brachman | Jacobs Technion-Cornell Institute |
| Andrew Clark | Molecular Biology and Genetics |
| Ben Cornwell | Sociology |
| David Easley | Economics |
| Peter Enns | Government |
| Michael Gore | Integrative Plant Sciences |
| Giles Hooker | Biological Statistics and Computational Biology |
| Thorsten Joachims | Computer Science and Information Science |
| Jon Kleinberg | Computer Science and Information Science |
| Jura Liaukonyte | Applied Economics and Management |
| Oskar Liivak | Law |
| Christiane Linster | Neurobiology and Behavior |
| Steven Osofsky | Population Medicine and Diagnostic Sciences |
| Jyotishman Pathak | Weill Department of Medicine |
| David Shmoys | Operations Research and Information Engineering |
| Aaron Wagner | Electrical and Computer Engineering |
| Harel Weinstein | Weill Department of Medicine |
| Marty Wells | Biological Statistics and Computational Biology |
| Peter Wittich | Physics |
More Information
- December 2017 Data Science Task Force report (PDF)
- The Center for Data Science for Enterprise & Society website