David Madras
Impact in
- Health Informatics top 10%
- Artificial Intelligence in Healthcare and Education
- Safety Research top 10%
- Ethics and Social Impacts of AI
Papers in
-
- Explainable Artificial Intelligence (XAI) 5
- Adversarial Robustness in Machine Learning 3
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- Ethics and Social Impacts of AI 4
- Co-authors
- Richard S. Zemel (5 shared papers)Toniann Pitassi (5 shared papers)Elliot Creager (3 shared papers)Kevin Swersky (1 shared paper)Jörn-Henrik Jacobsen (1 shared paper)Dylan Hadfield-Menell (1 shared paper)Aparna Balagopalan (1 shared paper)Gillian K. Hadfield (1 shared paper)
- Journals
- Science Advances (1 paper)SHILAP Revista de lepidopterología (1 paper)arXiv (Cornell University) (3 papers)eScholarship (California Digital Library) (1 paper)International Conference on Machine Learning (1 paper)
- Partner nations
- CanadaUnited StatesGermany
In The Last Decade
David Madras
7 papers receiving 128 citations
Peers
Comparison fields: 5 of 45
- Health Informatics 19
- Safety Research 52
- Artificial Intelligence 88
- Computer Science Applications 7
- Computer Vision and Pattern Recognition 21
Countries citing papers authored by David Madras
This map shows the geographic impact of David Madras'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 David Madras with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites David Madras more than expected).
Fields of papers citing papers by David Madras
This network shows the impact of papers produced by David Madras. 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 David Madras. The network helps show where David Madras may publish in the future.
Co-authors
The 20 scholars most cited alongside David Madras, 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 | 46 | |
| 2 | 2019 | 44 | |
| 3 | 2017 | 29 | |
| 4 | 2023 | 8 | |
| 5 | Predict Responsibly: Increasing Fairness by Learning To Defer | 2017 | 2 |
| 6 | On Meaningful Human Control in High-Stakes Machine-Human Partnerships | 2019 | 2 |
| 7 | Causal Modeling for Fairness In Dynamical Systems | 2020 | 1 |
| 8 | 2018 | 1 | |
| 9 | 2024 | 0 |
About David Madras
David Madras is a scholar working on Artificial Intelligence, Safety Research, Economics and Econometrics, Statistics and Probability and Surgery, having authored 9 papers that have together received 133 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (5 papers), Ethics and Social Impacts of AI (4 papers), Adversarial Robustness in Machine Learning (3 papers), Advanced Causal Inference Techniques (2 papers), Health Systems, Economic Evaluations, Quality of Life (1 paper), Multimodal Machine Learning Applications (1 paper), Sports Dynamics and Biomechanics (1 paper) and Face recognition and analysis (1 paper). The work is most often cited by research in Health Informatics (19 citations), Safety Research (52 citations), Artificial Intelligence (88 citations), Computer Science Applications (7 citations) and Computer Vision and Pattern Recognition (21 citations). David Madras has collaborated with scholars based in Canada, United States and Germany. Frequent co-authors include Richard S. Zemel, Toniann Pitassi, Elliot Creager, Kevin Swersky, Jörn-Henrik Jacobsen, Dylan Hadfield-Menell, Aparna Balagopalan, Gillian K. Hadfield, Marzyeh Ghassemi and S Lepage. Their work appears in journals such as Science Advances, SHILAP Revista de lepidopterología, arXiv (Cornell University), eScholarship (California Digital Library) 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.