DOE Randomized Algorithms for Combinatorial Scientific Computing
Harvard Pre-Proposal Deadline: May 1, 2022
FAS/SEAS/OSP Pre-Application Deadline: May 16, 2022
Sponsor Pre-Application Deadline: May 19, 2022
FAS/SEAS/OSP Full Proposal Deadline: June 23, 2022
Sponsor Full Proposal Deadline: June 30, 2022
Award Amount: Up to $800,000/year for three years

The DOE SC program in Advanced Scientific Computing Research (ASCR) has announced its interest in basic research in the design, development, analysis, and scalability of randomized algorithms for the challenging discrete and combinatorial problems that arise in the Department’s energy, environmental, and national security mission areas. The overarching goal of randomized algorithms research, under this Funding Opportunity Announcement, is to find scalable ways to sample, organize, search, or analyze very large data streams, discrete structures, and combinatorial problems relevant to DOE mission areas. The five research topics of interest focus on algorithms for discrete and combinatorial problems:

1. Randomized algorithms for discrete problems that cannot be modeled as networks
2. Randomized algorithms for solving well-defined problems on networks
3. Universal sketching and sampling on discrete data
4. Randomized algorithms for combinatorial and discrete optimization
5. Randomized algorithms for machine learning on networks

Applications submitted in response to this FOA must substantively address one (or more) of the above five research topics and the following three facets of randomized algorithms for discrete and combinatorial scientific computing:

1. Impact: What are the most significant or compelling scientific or technical challenges that are driving the development of the randomized algorithms approach?
2. Methodology: In what ways does the randomized approach provide a new and/or significant enabling technology for scientific computing? What are the potential merits and limitations of the randomized approach, particularly with respect to current and emerging high-performance computing architectures and ecosystems?
3. Validation: What is a relevant set of non-trivial metrics for assessing the accuracy and effectiveness of the randomized approaches?