Zachary Wu

Molecular Biology · 2018–2021

A researcher in the Science corpus: 7 papers; trusts 70 researchers and is trusted by 8 researchers here, as inferred from the citation graph.

Subfields

• Molecular Biology — 174 citations into it; #5,546 of 17,773 by trust here, #3,261 by citations
• Computational Biology & Bioinformatics — 75 citations into it; #339 of 660 by trust here, #218 by citations
• Artificial Intelligence — 42 citations into it; #23,233 of 35,345 by trust here, #13,760 by citations
• Materials Chemistry — 35 citations into it
• Generative Models — 29 citations into it
• Computer Vision and Pattern Recognition — 28 citations into it
• Computational Theory and Mathematics — 21 citations into it
• Representation & Self-Supervised Learning — 17 citations into it; #2,709 of 3,145 by trust here, #2,066 by citations

Whom Zachary Wu trusts overall

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

1. Kevin Kaichuang Yang
2. Frances H. Arnold
3. Philip A. Romero
4. Claire N. Bedbrook
5. Geoffrey E. Hinton
6. Kevin P. Murphy
7. Yoshua Bengio
8. Diederik P. Kingma
9. Jerome H. Friedman
10. UniProt Consortium

Who trusts Zachary Wu overall

1. Kevin Kaichuang Yang
2. Yunan Luo
3. Konstantin Weissenow
4. Ali Madani
5. Tristan Bepler
6. Stanislav Mazurenko
7. Frances H. Arnold
8. Christian Dallago
9. Lam Vo
10. Chloe Hsu

Papers from Zachary Wu's perspective

Ordered by how strongly the researchers Zachary Wu trusts endorse each paper. Same corpus, a different order for every researcher.

1. Deep Residual Learning for Image Recognition (Computer Vision and Pattern Recognition)
2. ImageNet classification with deep convolutional neural networks
3. Attention is All you Need (Neural Information Processing Systems)
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. Long Short-Term Memory
6. Gradient-based learning applied to document recognition
7. ImageNet: A large-scale hierarchical image database
8. GENERATIVE ADVERSARIAL NETS
9. Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift (International Conference on Machine Learning)
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Learned protein embeddings for machine learning (2018) — 132 citations
• Machine-learning-guided directed evolution for protein engineering (2018) — 993 citations
• Highly accurate protein structure prediction for the human proteome (2021) — 2,524 citations
• Protein sequence design with deep generative models (2021) — 165 citations
• Machine learning-assisted directed protein evolution with combinatorial libraries (2019) — 515 citations
• Learned protein embeddings for machine learning (2018) — 195 citations
• Machine learning in protein engineering (2018) — 33 citations