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Craig Pelissier
Researcher at Goddard Space Flight Center
Publications - 8
Citations - 230
Craig Pelissier is an academic researcher from Goddard Space Flight Center. The author has contributed to research in topics: Computer science & Quantum computer. The author has an hindex of 3, co-authored 8 publications receiving 70 citations.
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Journal ArticleDOI
What Role Does Hydrological Science Play in the Age of Machine Learning
Grey Nearing,Frederik Kratzert,Alden Keefe Sampson,Craig Pelissier,Daniel Klotz,Jonathan Frame,Cristina Prieto,Hoshin V. Gupta +7 more
TL;DR: This commentary is a call to action for the hydrology community to focus on developing a quantitative understanding of where and when hydrological process understanding is valuable in a modeling discipline increasingly dominated by machine learning.
Posted ContentDOI
Designing Peptides on a Quantum Computer
Vikram Khipple Mulligan,Hans Melo,Haley Irene Merritt,Stewart Slocum,Brian D. Weitzner,Andrew M. Watkins,P. Douglas Renfrew,Craig Pelissier,Paramjit S. Arora,Richard Bonneau +9 more
TL;DR: This work presents a system whereby Rosetta, a state-of-the-art protein design software suite, interfaces with the D-Wave quantum processing unit to find amino acid side chain identities and conformations to stabilize a fixed protein backbone, using a large side-chain rotamer library and the full Rosetta energy function.
Posted Content
Combining Parametric Land Surface Models with Machine Learning.
TL;DR: A path is outlined for using hybrid modeling to build global land-surface models with the potential to significantly outperform the current state-of-the-art and avoids bad predictions in scenarios where similar training data is not available.
Proceedings ArticleDOI
Quantum Assisted Image Registration
TL;DR: In this article, a mapping of image matching to a quadratic-unconstrained-binary optimization problem and a machine learning approach that employs generative models trained using quantum annealing statistics are presented.
Proceedings ArticleDOI
Combining Parametric Land Surface Models with Machine Learning
TL;DR: In this article, a hybrid machine learning and process-based modeling (PBM) approach is proposed and evaluated at a handful of AmeriFlux sites to simulate the top-layer soil moisture state.