Amos Azaria
Impact in
- Health Informatics top 5%
-
- Mobile Crowdsensing and Crowdsourcing
Papers in
- Co-authors
- Sarit KrausV. S. SubrahmanianRina AzoulayTom M. MitchellBrad A. MyersToby Jia-Jun LiEmilio FerraraAlessandro Flammini
- Journals
- Autonomous Agents and Multi-Agent Systems (4 papers)Sensors (4 papers)IEEE Access (3 papers)Applied Sciences (2 papers)Synthese (1 paper)
- Partner nations
- IsraelUnited StatesPoland
In The Last Decade
Amos Azaria
68 papers receiving 1.2k citations
Hit Papers
Peers
Comparison fields: 5 of 106
- Health Informatics 32
- Computer Science Applications 109
- Artificial Intelligence 548
- Information Systems 370
- General Decision Sciences 23
Countries citing papers authored by Amos Azaria
This map shows the geographic impact of Amos Azaria'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 Amos Azaria with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Amos Azaria more than expected).
Fields of papers citing papers by Amos Azaria
This network shows the impact of papers produced by Amos Azaria. 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 Amos Azaria. The network helps show where Amos Azaria may publish in the future.
Co-authors
The 25 scholars most cited alongside Amos Azaria, 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 | 2024 | 0 | |
| 2 | 2024 | 1 | |
| 3 | 2023 | 1 | |
| 4 | 2023 | 13 | |
| 5 | 2022 | 2 | |
| 6 | 2021 | 0 | |
| 7 | 2021 | 1 | |
| 8 | 2021 | 70 | |
| 9 | 2021 | 4 | |
| 10 | 2020 | 15 | |
| 11 | 2020 | 2 | |
| 12 | Safebot: A Safe Collaborative Chatbot. | 2018 | 2 |
| 13 | 2017 | 13 | |
| 14 | 2015 | 1 | |
| 15 | 2015 | 11 | |
| 16 | 2014 | 4 | |
| 17 | 2014 | 6 | |
| 18 | 2014 | 4 | |
| 19 | Movie recommender system for profit maximization (short LBP) | 2013 | 1 |
| 20 | 2011 | 26 |
About Amos Azaria
Amos Azaria is a scholar working on Health Informatics, General Decision Sciences, Computer Science Applications, Management Science and Operations Research and Automotive Engineering, having authored 76 papers that have together received 1.2k indexed citations. Recurring topics across this work include Transportation and Mobility Innovations (11 papers), Auction Theory and Applications (11 papers), Topic Modeling (8 papers), Natural Language Processing Techniques (8 papers), Mobile Crowdsensing and Crowdsourcing (8 papers), Transportation Planning and Optimization (7 papers), Game Theory and Voting Systems (7 papers) and Experimental Behavioral Economics Studies (6 papers). The work is most often cited by research in Health Informatics (32 citations), Computer Science Applications (109 citations), Artificial Intelligence (548 citations), Information Systems (370 citations) and General Decision Sciences (23 citations). Amos Azaria has collaborated with scholars based in Israel, United States and Poland. Frequent co-authors include Sarit Kraus, V. S. Subrahmanian, Rina Azoulay, Tom M. Mitchell, Brad A. Myers, Toby Jia-Jun Li, Emilio Ferrara, Alessandro Flammini, Claudia V. Goldman and Kristina Lerman. Their work appears in journals such as Autonomous Agents and Multi-Agent Systems, Sensors, IEEE Access, Applied Sciences and Synthese.
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.