Ryan Poplin

Molecular Biology · 2011–2025

A researcher in the Science corpus: 15 papers; trusts 16 researchers and is trusted by 1 researcher here, as inferred from the citation graph.

Subfields

• Molecular Biology — 172 citations into it; #17,325 of 17,773 by trust here, #627 by citations
• Artificial Intelligence — 76 citations into it
• Computer Vision and Pattern Recognition — 48 citations into it; #21,415 of 21,575 by trust here, #8,205 by citations
• Computational Biology & Bioinformatics — 26 citations into it
• Ecology — 22 citations into it
• Model Architectures — 18 citations into it
• Safety, Robustness & Interpretability — 17 citations into it; #2,234 of 2,251 by trust here, #1,205 by citations

Whom Ryan Poplin trusts overall

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

1. Heng Li
2. Erik P. Garrison
3. Richard Durbin
4. Christian Szegedy
5. Alex Krizhevsky
6. Geoffrey E. Hinton
7. Diederik P. Kingma
8. Gabor T. Marth
9. Mark A. DePristo
10. Temple F. Smith

Who trusts Ryan Poplin overall

1. Raquel Dias
2. Arvid Lundervold
3. Sara Beery
4. Nicolas C. Rochette
5. Eric J. Topol
6. Wan Zhu
7. Joe G. Greener
8. Ursula Schmidt-Erfurth
9. Ali Torkamani
10. Heng Li

Papers from Ryan Poplin's perspective

Ordered by how strongly the researchers Ryan Poplin 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. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
3. ImageNet classification with deep convolutional neural networks
4. Long Short-Term Memory
5. Gradient-based learning applied to document recognition
6. Going deeper with convolutions
7. Attention is All you Need (Neural Information Processing Systems)
8. ImageNet: A large-scale hierarchical image database
9. Dropout: a simple way to prevent neural networks from overfitting
10. Learning Multiple Layers of Features from Tiny Images

Their papers

• Likelihood Ratios for Out-of-Distribution Detection (2019) — 840 citations
• A framework for variation discovery and genotyping using next-generation DNA sequencing data (2011) — 11,249 citations
• A global reference for human genetic variation (2015) — 16,925 citations
• From FastQ data to high confidence variant calls: the Genome Analysis Toolkit best practices pipeline (2013) — 6,636 citations
• Creating a universal SNP and small indel variant caller with deep neural networks (2016) — 103 citations
• The genetic architecture of type 2 diabetes (2016) — 1,066 citations
• Scaling accurate genetic variant discovery to tens of thousands of samples (2017) — 1,758 citations
• A deep learning approach to pattern recognition for short DNA sequences (2018) — 59 citations
• Gemini 2.5: Pushing the Frontier with Advanced Reasoning, Multimodality, Long Context, and Next Generation Agentic Capabilities (2025) — 3,609 citations
• Identifying viruses from metagenomic data using deep learning (2020) — 553 citations
• A universal SNP and small-indel variant caller using deep neural networks (2018) — 1,463 citations
• In silico labeling: Predicting fluorescent labels in unlabeled images (2018) — 587 citations
• Predicting Cardiovascular Risk Factors from Retinal Fundus Photographs using Deep Learning (2017) — 1,503 citations
• Patterns and rates of exonic de novo mutations in autism spectrum disorders (2012) — 1,772 citations
• An integrated map of genetic variation from 1,092 human genomes (2012) — 8,008 citations