Scholar
Rick Archibald
Google Scholar ID: 65z2UVUAAAAJ
ORNL
Applied Mathematics & High Performance Computing
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Citations
1,821
H-index
24
i10-index
41
Publications
20
Co-authors
97
list available
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ARCHIBALDRK@ORNL.GOV
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Publications
2 items
Exploring the Capabilities of the Frontier Large Language Models for Nuclear Energy Research
Cited
0
A General Framework for Error-controlled Unstructured Scientific Data Compression
IEEE International Conference on e-Science · 2024
Cited
1
Resume
Academic Achievements
Householder Fellowship (2005–2007)
Published multiple high-impact papers, including:
“Uncertainty-aware inverse modeling for federated scientific discovery across DOE facilities” (June 2025, Conference Paper)
“Assimilating partial observation to enhance feedback control of stochastic dynamical systems...” (February 2025, Foundations of Data Science)
“Streaming Compression of Scientific Data via Weak-SINDy” (February 2025, SIAM Journal on Scientific Computing)
“Privacy Preserving Federated Learning for Advanced Scientific Ecosystems” (December 2024, Conference Paper)
“A Framework for Compressing Unstructured Scientific Data via Serialization” (December 2024, Conference Paper)
Co-authors
8 total
Anne Gelb
John G. Kemeny Parents Professor of Mathematics, Dartmouth College
Sergei V. Kalinin
Weston Fulton Chair Professor, UT Knoxville. Chief Scientist, AI/ML for Physical Sciences, PNNL
Feng Bao
Associate Professor of Mathematics, Florida State University
Rama Vasudevan
R&D Staff, Center for Nanophase Materials Sciences at Oak Ridge National
Christopher T. Symons
Lirio
Scott Klasky
Oak Ridge National Laboratory
Qian Gong
Oak Ridge National Lab, Fermilab, Duke University
Kody J. H. Law
Professor at the University of Manchester and AI Research Scientist at Meta