Harini Suresh

Artificial Intelligence · 2018–2021

A researcher in the Science corpus: 5 papers; trusts 86 researchers and is trusted by 11 researchers here, as inferred from the citation graph.

Subfields

• Artificial Intelligence — 147 citations into it; #21,350 of 35,345 by trust here, #11,750 by citations
• Safety, Robustness & Interpretability — 68 citations into it
• Computer Vision and Pattern Recognition — 52 citations into it
• Safety Research — 42 citations into it; #5,197 of 5,829 by trust here, #4,008 by citations
• Evaluation & Benchmarks — 40 citations into it
• Medical & Health AI — 34 citations into it; #140 of 239 by trust here, #221 by citations
• AI Ethics, Fairness & Society — 25 citations into it; #230 of 494 by trust here, #247 by citations
• Sociology and Political Science — 19 citations into it

Whom Harini Suresh trusts overall

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

1. Cynthia Dwork
2. Sam Corbett-Davies
3. Jia Deng
4. Alexandra Chouldechova
5. Andrew Gelman
6. R. Caruana
7. Faisal Kamiran
8. Toon Calders
9. Sepp Hochreiter
10. Richard S. Zemel

Who trusts Harini Suresh overall

1. Ryan Shaun Joazeiro de Baker
2. Tanmay Garg
3. Ameya Vaidya
4. Ninareh Mehrabi
5. John V. Guttag
6. Aaron Hawn
7. Kimmo Kärkkäinen
8. Thomas Liao
9. Reva Schwartz
10. Sarah Masud

Papers from Harini Suresh's perspective

Ordered by how strongly the researchers Harini Suresh 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. Long Short-Term Memory
4. Adam: A Method for Stochastic Optimization (International Conference on Learning Representations)
5. ImageNet: A large-scale hierarchical image database
6. Gradient-based learning applied to document recognition
7. Attention is All you Need (Neural Information Processing Systems)
8. Learning Multiple Layers of Features from Tiny Images
9. Very Deep Convolutional Networks for Large-Scale Image Recognition (International Conference on Learning Representations)
10. ImageNet Large Scale Visual Recognition Challenge

Their papers

• A Framework for Understanding Sources of Harm throughout the Machine Learning Life Cycle (2019) — 647 citations
• Underspecification Presents Challenges for Credibility in Modern Machine Learning (2020) — 897 citations
• Do as AI say: susceptibility in deployment of clinical decision-aids (2021) — 431 citations
• A Framework for Understanding Unintended Consequences of Machine Learning (2019) — 416 citations
• Learning Tasks for Multitask Learning: Heterogenous Patient Populations in the ICU (2018) — 92 citations