Nam-Ho Kim

Statistics and Probability · 2000–2016

A researcher in the Science corpus: 11 papers; trusts 40 researchers and is trusted by 35 researchers here, as inferred from the citation graph.

Subfields

• Statistics and Probability — 55 citations into it; #6,249 of 12,932 by trust here, #3,177 by citations
• Computational Theory and Mathematics — 43 citations into it; #10,696 of 12,182 by trust here, #3,924 by citations
• Management Science and Operations Research — 28 citations into it
• Artificial Intelligence — 21 citations into it
• Mechanics of Materials — 16 citations into it; #1,473 of 6,291 by trust here, #3,138 by citations
• Statistical and Nonlinear Physics — 13 citations into it; #8,758 of 10,127 by trust here, #9,324 by citations
• Civil and Structural Engineering — 12 citations into it; #400 of 6,160 by trust here, #2,117 by citations
• Probabilistic & Bayesian Methods — 10 citations into it

Whom Nam-Ho Kim trusts overall

Their strongest outbound trust, strongest first, inferred from their citations.

1. Kyung K. Choi
2. Bradley Efron
3. Gang-Won Jang
4. Radford M. Neal
5. Robert A. Jacobs
6. Emanuel Parzen
7. Soumendra Nath Lahiri
8. Jiun-Shyan Chen
9. Alexander I. J. Forrester
10. Loic Le Gratiet

Who trusts Nam-Ho Kim overall

1. F. Sonmez
2. Zhen Luo
3. Yaser Kiani
4. B. Kiefer
5. Allert Bosch
6. Son Thai
7. Kyung K. Choi
8. A. M. D’Altri
9. M. R. Eslami
10. Yang Yang

Papers from Nam-Ho Kim's perspective

Ordered by how strongly the researchers Nam-Ho Kim trusts endorse each paper. Same corpus, a different order for every researcher.

1. Bayesian learning for neural networks
2. Design and analysis of computer experiments
3. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
4. A Taxonomy of Global Optimization Methods Based on Response Surfaces
5. ImageNet classification with deep convolutional neural networks
6. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
7. Predicting the output from a complex computer code when fast approximations are available
8. Monte Carlo Statistical Methods
9. Learning Multiple Layers of Features from Tiny Images
10. Gradient-based learning applied to document recognition

Their papers

• Adaptive Designs of Experiments for Accurate Approximation of a Target Region (2010) — 330 citations
• Meshless shape design sensitivity analysis and optimization for contact problem with friction (2000) — 55 citations
• Review of options for structural design sensitivity analysis. Part 1: Linear systems (2005) — 351 citations
• Review of multi-fidelity models (2016) — 293 citations
• Practical options for selecting data-driven or physics-based prognostics algorithms with reviews (2015) — 436 citations
• Introduction to Nonlinear Finite Element Analysis (2014) — 517 citations
• Application of bootstrap method in conservative estimation of reliability with limited samples (2010) — 99 citations
• Efficient Shape Optimization Under Uncertainty Using Polynomial Chaos Expansions and Local Sensitivities (2006) — 60 citations
• Eulerian shape design sensitivity analysis and optimization with a fixed grid (2005) — 66 citations
• Structural Sensitivity Analysis and Optimization 1: Linear Systems (2005) — 561 citations
• Numerical method for shape optimization using meshfree method (2002) — 73 citations