Seth Neel
Impact in
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- Artificial Intelligence in Healthcare and Education
- Safety Research top 10%
- Ethics and Social Impacts of AI
Papers in
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- Privacy-Preserving Technologies in Data 4
- Adversarial Robustness in Machine Learning 3
- Reinforcement Learning in Robotics 2
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- Ethics and Social Impacts of AI 4
- Co-authors
- Aaron Roth (11 shared papers)Michael Kearns (5 shared papers)Zhiwei Steven Wu (5 shared papers)Christopher Jung (4 shared papers)Katrina Ligett (3 shared papers)Shahin Jabbari (1 shared paper)Bo Waggoner (2 shared papers)Logan Stapleton (2 shared papers)
- Journals
- SHILAP Revista de lepidopterología (1 paper)DROPS (Schloss Dagstuhl – Leibniz Center for Informatics) (1 paper)arXiv (Cornell University) (4 papers)International Conference on Machine Learning (1 paper)
- Partner nations
- United StatesIsraelUnited Kingdom
In The Last Decade
Seth Neel
12 papers receiving 115 citations
Peers
Comparison fields: 5 of 27
- Health Informatics 8
- Safety Research 45
- Artificial Intelligence 92
- Computer Science Applications 13
- Management Science and Operations Research 19
Countries citing papers authored by Seth Neel
This map shows the geographic impact of Seth Neel's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Seth Neel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Seth Neel more than expected).
Fields of papers citing papers by Seth Neel
This network shows the impact of papers produced by Seth Neel. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Seth Neel. The network helps show where Seth Neel may publish in the future.
Co-authors
The 12 scholars most cited alongside Seth Neel, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2019 | 28 | |
| 2 | Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness | 2017 | 25 |
| 3 | 2017 | 14 | |
| 4 | Eliciting and Enforcing Subjective Individual Fairness. | 2019 | 13 |
| 5 | 2018 | 12 | |
| 6 | 2019 | 8 | |
| 7 | 2023 | 6 | |
| 8 | Descent-to-Delete: Gradient-Based Methods for Machine Unlearning | 2020 | 4 |
| 9 | 2021 | 4 | |
| 10 | 2021 | 2 | |
| 11 | 2019 | 2 | |
| 12 | Differentially Private Objective Perturbation: Beyond Smoothness and Convexity | 2019 | 1 |
About Seth Neel
Seth Neel is a scholar working on Artificial Intelligence, Safety Research, Management Science and Operations Research, Statistics and Probability and Computer Networks and Communications, having authored 12 papers that have together received 119 indexed citations. Recurring topics across this work include Ethics and Social Impacts of AI (4 papers), Privacy-Preserving Technologies in Data (4 papers), Adversarial Robustness in Machine Learning (3 papers), Auction Theory and Applications (2 papers), Reinforcement Learning in Robotics (2 papers), Advanced Causal Inference Techniques (2 papers), Single-cell and spatial transcriptomics (1 paper) and Statistical Methods and Bayesian Inference (1 paper). The work is most often cited by research in Health Informatics (8 citations), Safety Research (45 citations), Artificial Intelligence (92 citations), Computer Science Applications (13 citations) and Management Science and Operations Research (19 citations). Seth Neel has collaborated with scholars based in United States, Israel and United Kingdom. Frequent co-authors include Aaron Roth, Michael Kearns, Zhiwei Steven Wu, Christopher Jung, Katrina Ligett, Shahin Jabbari, Bo Waggoner, Logan Stapleton, Steven Y. Wu and Jamie Morgenstern. Their work appears in journals such as SHILAP Revista de lepidopterología, DROPS (Schloss Dagstuhl – Leibniz Center for Informatics), arXiv (Cornell University) and International Conference on Machine Learning.
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive bibliographic database. While OpenAlex provides broad and valuable coverage of the global research landscape, it—like all bibliographic datasets—has inherent limitations. These include incomplete records, variations in author disambiguation, differences in journal indexing, and delays in data updates. As a result, some metrics and network relationships displayed in Rankless may not fully capture the entirety of a scholar's output or impact.