The Big Data paradigm has emerged as a result of the vast amounts of data that are becoming available across science, business, and government. Realizing the potential of big data will require fundamentally new techniques and technologies to handle the complexity, size, and rate of generation of these data. Principled, innovative approaches are needed to address the challenges associated with the management, modeling, and analysis of such unprecedented amounts of data, including automation of aspects of the data-enabled discovery processes, development of new computational, mathematical, and statistical methods for data analysis, and creation of novel visualization techniques for drawing insights from data.
The National Science Foundation has released a revised version of the solicitation “Critical Techniques and Technologies for Advancing Foundations and Applications of Big Data Science & Engineering (BIGDATA).” All the NSF Directorates, including the Directorate of Mathematical and Physical Sciences (MPS), are participating in this crosscutting initiative. Xiaoming Huo and Nandini Kannan from the Division of Mathematical Sciences are among the cognizant program officers.
This solicitation should be of particular interest to the applied and computational mathematics communities, with its emphasis on both fundamental theoretical and methodological issues related to big data, as well as the development of algorithms, tools, and techniques for the analysis of big data from different application domains.
The BIGDATA program invites proposals in two categories:
Foundations (F): those that focus on the development of novel techniques, or novel theoretical analysis (including statistics and probability) or experimental evaluation of techniques, that are broadly applicable; and
Innovative Applications (IA): those that focus on the development of innovative techniques, methodologies, and technologies for specific application areas or innovative adaptations of existing techniques, methodologies and technologies to new application areas.
Potential research areas and challenges in big data include:
- Reproducibility, replicability, and uncertainty quantification
- Data confidentiality, privacy, and security issues as they relate to big data
- Generating hypotheses, explanations, and models from data
- Prioritizing, testing, scoring, and validating hypotheses
- Interactive data visualization techniques
- Scalable machine learning, statistical inference, and data mining
- Eliciting causal relations from observations and experiments
- Addressing foundational mathematical and statistical principles at the core of the new BIGDATA technologies
The applied and computational mathematics communities have played a major role in this area, and we hope that researchers from the community will take advantage of this new opportunity. The program page provides a link to the solicitation, the list of program officers representing the different directorates, and a link to recent awards made through this program. The solicitation provides information on proposal preparation and submission, review criteria, and required supplementary documentation. The deadline for submission of BIGDATA proposals is May 20, 2015.
Readers who are interested in serving as reviewers for proposals or who would like additional information are encouraged to contact Xiaoming Huo ([email protected]) or Nandini Kannan ([email protected]).