Peter Hase
Impact in
- Artificial Intelligence top 10%
- Explainable Artificial Intelligence (XAI)
- Topic Modeling
- Natural Language Processing Techniques
- Adversarial Robustness in Machine Learning
- Machine Learning and Data Classification
- Anomaly Detection Techniques and Applications
- Machine Learning in Healthcare
Papers in
-
- Topic Modeling 4
- Explainable Artificial Intelligence (XAI) 4
- Natural Language Processing Techniques 3
- Speech and dialogue systems 1
- Semantic Web and Ontologies 1
- Machine Learning and Data Classification 1
- Adversarial Robustness in Machine Learning 1
-
- Computational and Text Analysis Methods 1
- Co-authors
- Mohit Bansal (5 shared papers)Cynthia Rudin (1 shared paper)Chaofan Chen (1 shared paper)Oscar Li (1 shared paper)Shiyue Zhang (1 shared paper)Nazneen Fatema Rajani (2 shared papers)Caiming Xiong (1 shared paper)Han Guo (1 shared paper)
- Journals
- Proceedings of the AAAI Conference on Human Computation and Crowdsourcing (1 paper)Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (1 paper)
- Partner nations
- United StatesGermany
In The Last Decade
Peter Hase
6 papers receiving 133 citations
Peers
Comparison fields: 5 of 40
- Health Informatics 7
- Artificial Intelligence 119
- Computer Vision and Pattern Recognition 32
- Information Systems and Management 6
- Signal Processing 7
Countries citing papers authored by Peter Hase
This map shows the geographic impact of Peter Hase'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 Peter Hase with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Peter Hase more than expected).
Fields of papers citing papers by Peter Hase
This network shows the impact of papers produced by Peter Hase. 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 Peter Hase. The network helps show where Peter Hase may publish in the future.
Co-authors
The 18 scholars most cited alongside Peter Hase, 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 | 55 | |
| 2 | 2020 | 36 | |
| 3 | 2021 | 24 | |
| 4 | 2023 | 15 | |
| 5 | 2022 | 5 | |
| 6 | 1997 | 3 | |
| 7 | 2024 | 0 |
About Peter Hase
Peter Hase is a scholar working on Artificial Intelligence, General Social Sciences, Infectious Diseases, Organic Chemistry and Surgery, having authored 7 papers that have together received 138 indexed citations. Recurring topics across this work include Topic Modeling (4 papers), Explainable Artificial Intelligence (XAI) (4 papers), Natural Language Processing Techniques (3 papers), Speech and dialogue systems (1 paper), Semantic Web and Ontologies (1 paper), Machine Learning and Data Classification (1 paper), Computational and Text Analysis Methods (1 paper) and Adversarial Robustness in Machine Learning (1 paper). The work is most often cited by research in Health Informatics (7 citations), Artificial Intelligence (119 citations), Computer Vision and Pattern Recognition (32 citations), Information Systems and Management (6 citations) and Signal Processing (7 citations). Peter Hase has collaborated with scholars based in United States and Germany. Frequent co-authors include Mohit Bansal, Cynthia Rudin, Chaofan Chen, Oscar Li, Shiyue Zhang, Nazneen Fatema Rajani, Caiming Xiong, Han Guo, Zornitsa Kozareva and Veselin Stoyanov. Their work appears in journals such as Proceedings of the AAAI Conference on Human Computation and Crowdsourcing and Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing.
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.