Pablo Pedemonte
Impact in
- Health Informatics top 5%
- Artificial Intelligence in Healthcare and Education
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- Ethics and Social Impacts of AI
Papers in
-
- Explainable Artificial Intelligence (XAI) 3
- Adversarial Robustness in Machine Learning 2
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- Software Engineering Research 1
- Co-authors
- Ramya Raghavendra (4 shared papers)Samuel C. Hoffman (4 shared papers)Amit Dhurandhar (4 shared papers)Stephanie Houde (4 shared papers)Vijay Arya (4 shared papers)Q. Vera Liao (4 shared papers)Sami Mourad (4 shared papers)Moninder Singh (4 shared papers)
- Journals
- Journal of Machine Learning Research (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (1 paper)
- Partner nations
- United StatesIrelandArgentina
In The Last Decade
Pablo Pedemonte
7 papers receiving 115 citations
Peers
Comparison fields: 5 of 37
- Health Informatics 24
- Safety Research 26
- Artificial Intelligence 97
- Information Systems and Management 11
- Computer Science Applications 6
Countries citing papers authored by Pablo Pedemonte
This map shows the geographic impact of Pablo Pedemonte'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 Pablo Pedemonte with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pablo Pedemonte more than expected).
Fields of papers citing papers by Pablo Pedemonte
This network shows the impact of papers produced by Pablo Pedemonte. 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 Pablo Pedemonte. The network helps show where Pablo Pedemonte may publish in the future.
Co-authors
The 25 scholars most cited alongside Pablo Pedemonte, 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 | AI Explainability 360: An Extensible Toolkit for Understanding Data and Machine Learning Models | 2020 | 41 |
| 2 | 2020 | 37 | |
| 3 | 2020 | 22 | |
| 4 | 2022 | 11 | |
| 5 | 2019 | 7 | |
| 6 | 2011 | 3 | |
| 7 | 2012 | 2 |
About Pablo Pedemonte
Pablo Pedemonte is a scholar working on Artificial Intelligence, Information Systems, Health Informatics, Software and Computer Networks and Communications, having authored 7 papers that have together received 123 indexed citations. Recurring topics across this work include Explainable Artificial Intelligence (XAI) (3 papers), Artificial Intelligence in Healthcare and Education (2 papers), Adversarial Robustness in Machine Learning (2 papers), Scientific Computing and Data Management (1 paper), Software System Performance and Reliability (1 paper), Spreadsheets and End-User Computing (1 paper), Software Testing and Debugging Techniques (1 paper) and Software Engineering Research (1 paper). The work is most often cited by research in Health Informatics (24 citations), Safety Research (26 citations), Artificial Intelligence (97 citations), Information Systems and Management (11 citations) and Computer Science Applications (6 citations). Pablo Pedemonte has collaborated with scholars based in United States, Ireland and Argentina. Frequent co-authors include Ramya Raghavendra, Samuel C. Hoffman, Amit Dhurandhar, Stephanie Houde, Vijay Arya, Q. Vera Liao, Sami Mourad, Moninder Singh, Yunfeng Zhang and Pin‐Yu Chen. Their work appears in journals such as Journal of Machine Learning Research and Proceedings of the AAAI Conference on Artificial Intelligence.
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.