Randomized Algorithms for Combinatorial Scientific Computing
Event details
| Date | 30.06.2022 |
| Category | Call for proposal |
AIM: The DOE SC program in Advanced Scientific Computing Research (ASCR) hereby announces 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.
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.
AREAS OF INTEREST
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]:
ELIGIBILITY: EPFL researchers are eligible to participate as subawardee or as a prime if they possess skills, resources and abilities that do not exist among potential applicants in the U.S.
HOW TO APPLY: Get in touch with potential partners in the U.S. if you wish to participate as a partner in a collaborative project. To apply as a prime, pre-applications must be submitted electronically through the DOE SC Portfolio Analysis and Management System (PAMS) website https://pamspublic.science.energy.gov/ .
FOR FURTHER INFORMATION: Please refer to the DE-FOA-0002722 and contact the Research Office for any question and assistance.
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.
AREAS OF INTEREST
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
ELIGIBILITY: EPFL researchers are eligible to participate as subawardee or as a prime if they possess skills, resources and abilities that do not exist among potential applicants in the U.S.
HOW TO APPLY: Get in touch with potential partners in the U.S. if you wish to participate as a partner in a collaborative project. To apply as a prime, pre-applications must be submitted electronically through the DOE SC Portfolio Analysis and Management System (PAMS) website https://pamspublic.science.energy.gov/ .
FOR FURTHER INFORMATION: Please refer to the DE-FOA-0002722 and contact the Research Office for any question and assistance.
Practical information
- General public
- Free