Slots: 1 slot still available; 1 has been assigned to Itay Hen (Viterbi)
Deadlines
Internal Deadline: Contact ORIF.
LOI: May 19, 2022
External Deadline: June 30, 2022, 5pm PT
Award Information
Award Type: Grant / Cooperative Agreement
Estimated Number of Awards: 4-9
Anticipated Award Amount: $400,000 – $800,000 per year
Who May Serve as PI: Individuals with the skills, knowledge, and resources necessary to carry out the proposed research as a Principal Investigator (PI) are invited to work with their organizations to develop an application. Individuals from underrepresented groups as well as individuals with disabilities are always encouraged to apply.
Link to Award: https://science.osti.gov/grants/FOAs/-/media/grants/pdf/foas/2022/SC_FOA_0002722.pdf
Process for Limited Submissions
PIs must submit their application as a Limited Submission through the Office of Research Application Portal: https://orif.usc.edu/oor-portal/.
Materials to submit include:
- (1) Single Page Proposal Summary (0.5” margins; single-spaced; font type: Arial, Helvetica, or Georgia typeface; font size: 11 pt). Page limit includes references and illustrations. Pages that exceed the 1-page limit will be excluded from review.
- (2) CV – (5 pages maximum)
Note: The portal requires information about the PIs and Co-PIs in addition to department and contact information, including the 10-digit USC ID#, Gender, and Ethnicity. Please have this material prepared before beginning this application.
Purpose
Randomized algorithms are enabling advances in scientific machine learning and artificial intelligence (AI) for a wide range of “AI for Science” uses [1, 2]. Scientific discovery in priority areas such as climate science, astrophysics, fusion, materials design, combustion, and the Energy Earthshots initiative [3] will make use of increased understanding in randomized algorithms for surmounting the challenges of computational complexity, robustness, and scalability. Randomized algorithms also represent a major thrust for applied mathematics and computer science basic research, and such thrusts are essential for future progress in advanced scientific computing [4, 5, 6].
Randomized algorithms employ some form of randomness in internal algorithmic decisions to accelerate time to solution, increase scalability, or improve reliability. Examples include matrix sketching for solving large-scale least-squares problems and stochastic gradient descent for training scientific machine learning models. Rather than using heuristic or ad-hoc methods, the desired objective is the development of efficient randomized algorithms that have certificates of correctness and probabilistic guarantees of optimality or near-optimality.
ASCR held a four-day virtual workshop on “Randomized Algorithms for Scientific Computing (RASC)” on December 2-3, 2020 and January 6-7, 2021 [7]. The subsequent workshop report articulates how randomized algorithms research is motivated by application needs and drivers such as: Massive data from experiments, observations, and simulations; Forward problems; Inverse problems; Applications with discrete structure; Experimental designs; Software and libraries for scientific computing; Emerging hardware; and Scientific machine learning. For data collection, advances in imaging technologies – such as X-ray ptychography, electron microscopy, or electron energy loss spectroscopy – collect hyperspectral imaging and scattering data in terabytes and at high speeds enabled by state-of-the art detectors. The data collection is exceptionally fast relative to its analysis. Similarly, advances in high-performance computing and exascale systems have changed the nature of scientific computing research. An increasing
trend is that faster hardware makes data easier to generate, but more challenging to rapidly analyze. For problems with discrete structure – such as the Internet, power grids and biological networks – faster and better ways are needed to analyze, sample, manage, and sort discrete events, graphs, and data streams.
Research Area
The overarching goal of randomized algorithms research, under this Funding Opportunity Announcement (FOA), 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 as highlighted in the RASC workshop report [7, Section 3.3]:
- Randomized algorithms for discrete problems that cannot be modeled as networks
- Randomized algorithms for solving well-defined problems on networks
- Universal sketching and sampling on discrete data
- Randomized algorithms for combinatorial and discrete optimization
- Randomized algorithms for machine learning on networks
Note that connected structures such as graphs, hypergraphs and simplicial complexes are
collectively referred to as “networks.” An important crosscutting theme is verification and
validation for assessing the accuracy and reliability of the proposed randomized algorithms.
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;- Impact: What are the most significant or compelling scientific or technical challenges that
are driving the development of the randomized algorithms approach? - 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? - Validation: What is a relevant set of non-trivial metrics for assessing the accuracy and effectiveness of the randomized approaches?
- Impact: What are the most significant or compelling scientific or technical challenges that
Visit our Institutionally Limited Submission webpage for more updates and other announcements