C. Girard
Biomedical Engineering · with Quidant · 2005–2020
A researcher in the Science corpus: 6 papers; trusts 11 researchers and is trusted by 9 researchers here, as inferred from the citation graph.
Subfields
- Biomedical Engineering — 70 citations into it; #1,874 of 10,734 by trust here, #2,994 by citations
- Electronic, Optical and Magnetic Materials — 58 citations into it; #2,802 of 2,886 by trust here, #1,746 by citations
- Atomic and Molecular Physics, and Optics — 46 citations into it; #1,823 of 7,135 by trust here, #3,118 by citations
- Electrical and Electronic Engineering — 38 citations into it
- Molecular Biology — 14 citations into it
- Surfaces, Coatings and Films — 9 citations into it
- Aerospace Engineering — 6 citations into it
Whom C. Girard trusts overall
Their strongest outbound trust, strongest first, inferred from their citations.
- S. A. Maier
- Kalyanmoy Deb
- P. Kik
- F. García-Vidal
- Jiaqi Jiang
- Alain Dereux
- George C. Schatz
- William L. Barnes
- Stanley J. Osher
- Jakob S. Jensen
Who trusts C. Girard overall
- Alexandre Vial
- T. Laroche
- P. Polimeno
- Peter R. Wiecha
- G. Baffou
- A. Magazzù
- S. Gaponenko
- M. Iatì
- R. Quidant
Papers from C. Girard's perspective
Ordered by how strongly the researchers C. Girard trusts endorse each paper. Same corpus, a different order for every researcher.
- U-Net: Convolutional Networks for Biomedical Image Segmentation (International Conference on Medical Image Computing and Computer-Assisted Intervention)
- Language Models are Few-Shot Learners (Neural Information Processing Systems)
- Surface plasmon subwavelength optics (Nature)
- Topology optimization for nano‐photonics
- Multi-objective optimization using evolutionary algorithms
- Inception-v4, Inception-ResNet and the Impact of Residual Connections on Learning
- Physics-Constrained Deep Learning for High-dimensional Surrogate Modeling and Uncertainty Quantification without Labeled Data
- Fronts propagating with curvature-dependent speed: algorithms based on Hamilton-Jacobi formulations
- Deep neural networks for the evaluation and design of photonic devices
- Integrated photonic quantum technologies (Nature Photonics)
Their papers
- Heat generation in plasmonic nanostructures: Influence of morphology (2009) — 549 citations
- Parallel and selective trapping in a patterned plasmonic landscape (2007) — 481 citations
- Near fields in nanostructures (2005) — 210 citations
- Thermoplasmonics modeling: A Green's function approach (2010) — 151 citations
- Deep learning in nano-photonics: inverse design and beyond (2020) — 348 citations
- One‐Dimensional Plasmon Coupling by Facile Self‐Assembly of Gold Nanoparticles into Branched Chain Networks (2005) — 364 citations