Emma Pierson

Artificial Intelligence · 2015–2023

A researcher in the Science corpus: 11 papers; trusts 116 researchers and is trusted by 46 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 97 citations into it
• Sociology and Political Science — 53 citations into it
• Molecular Biology — 46 citations into it; #982 of 17,773 by trust here, #5,405 by citations
• Safety, Robustness & Interpretability — 42 citations into it
• Computer Vision and Pattern Recognition — 40 citations into it
• Statistics and Probability — 34 citations into it
• AI Ethics, Fairness & Society — 20 citations into it; #200 of 494 by trust here, #29 by citations
• Political Science and International Relations — 18 citations into it

Whom Emma Pierson trusts overall

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

1. Radford M. Neal
2. Geoffrey E. Hinton
3. Joseph B. Kruskal
4. Michael Betancourt
5. Michael I. Jordan
6. M. Chris Jones
7. Alex Krizhevsky
8. Sharad Goel
9. Michael E. Tipping
10. Andrew Gelman

Who trusts Emma Pierson overall

1. Romain Lopez
2. Sam Corbett-Davies
3. Jeffrey C. Regier
4. Tallulah S. Andrews
5. Sharad Goel
6. Rhonda L. Bacher
7. Il-Youp Kwak
8. Chieh Hua Lin
9. Luke Zappia
10. Peijie Lin

Papers from Emma Pierson's perspective

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

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

Their papers

• Ethical Machine Learning in Healthcare (2020) — 577 citations
• ZIFA: Dimensionality reduction for zero-inflated single-cell gene expression analysis (2015) — 578 citations
• Visualization and analysis of single-cell RNA-seq data by kernel-based similarity learning (2016) — 650 citations
• Denoising genome-wide histone ChIP-seq with convolutional neural networks (2016) — 64 citations
• Concept Bottleneck Models (2020) — 1,388 citations
• A large-scale analysis of racial disparities in police stops across the United States (2020) — 449 citations
• Coarse race data conceals disparities in clinical risk score performance (2023) — 36 citations
• Mobility network models of COVID-19 explain inequities and inform reopening (2020) — 1,461 citations
• Fast Threshold Tests for Detecting Discrimination (2017) — 53 citations
• Gender differences in beliefs about algorithmic fairness (2017) — 19 citations
• Algorithmic Decision Making and the Cost of Fairness (2017) — 1,410 citations