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SUMMARY:Randomized Algorithms for Combinatorial Scientific Computing
DTSTART;VALUE=DATE:20220630
DTSTAMP:20260405T220644Z
UID:1ef1e6cf8da5575534466ba5bf7fe3ad1b5d3f037d6626b6b2e6bd3f
CATEGORIES:Call for proposal
DESCRIPTION:AIM: The DOE SC program in Advanced Scientific Computing Resea
 rch (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 Departm
 ent’s energy\, environmental\, and national security mission areas.\nRan
 domized algorithms are enabling advances in scientific machine learning an
 d artificial intelligence (AI) for a wide range of “AI for Science” us
 es [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 rand
 omized algorithms for surmounting the challenges of computational complexi
 ty\, 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].\nRandomized algorithms employ some form of randomn
 ess in internal algorithmic decisions to accelerate time to solution\, inc
 rease scalability\, or improve reliability. Examples include matrix sketch
 ing for solving large-scale least-squares problems and stochastic gradient
  descent for training scientific machine learning models. Rather than usin
 g heuristic or ad-hoc methods\, the desired objective is the development o
 f efficient randomized algorithms that have certificates of correctness an
 d probabilistic guarantees of optimality or near-optimality.\n\nAREAS OF I
 NTEREST\nThe overarching goal of randomized algorithms research\, under th
 is Funding Opportunity Announcement (FOA)\, is to find scalable ways to sa
 mple\, organize\, search\, or analyze very large data streams\, discrete s
 tructures\, and combinatorial problems relevant to DOE mission areas. The 
 five research topics of interest focus on algorithms for discrete and comb
 inatorial problems as highlighted in the RASC workshop report [7\, Section
  3.3]:\n\n	Randomized algorithms for discrete problems that cannot be mode
 led as networks\n	Randomized algorithms for solving well-defined problems 
 on networks\n	Universal sketching and sampling on discrete data\n	Randomiz
 ed algorithms for combinatorial and discrete optimization\n	Randomized alg
 orithms for machine learning on networks \n\nDEADLINE: A pre-application (
 two pages) is required and must be submitted by May 19\, 2022 at 5:00 PM E
 T. Submission Deadline for Applications is June 30\, 2022 at 11:59 PM ET.\
 n\nELIGIBILITY: EPFL researchers are eligible to participate as subawardee
  or as a prime if they possess skills\, resources and abilities that do no
 t exist among potential applicants in the U.S.\n\nHOW TO APPLY: Get in tou
 ch with potential partners in the U.S. if you wish to participate as a par
 tner in a collaborative project. To apply as a prime\, pre-applications mu
 st be submitted electronically through the DOE SC Portfolio Analysis and M
 anagement System (PAMS) website https://pamspublic.science.energy.gov/ .\n
 \nFOR FURTHER INFORMATION: Please refer to the DE-FOA-0002722 and contact 
 the Research Office for any question and assistance.\n\n 
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STATUS:CONFIRMED
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