Doris Xin
Impact in
- Health Informatics top 10%
-
- Scientific Computing and Data Management
Papers in
-
- Machine Learning and Data Classification 4
- Data Stream Mining Techniques 2
- Machine Learning and Algorithms 2
- Advanced Graph Neural Networks 2
- Co-authors
- Aditya Parameswaran (5 shared papers)Stephen Macke (3 shared papers)Niloufar Salehi (1 shared paper)Deepak Agarwal (1 shared paper)Bo Long (1 shared paper)Jonathan Traupman (1 shared paper)Hui Miao (1 shared paper)Neoklis Polyzotis (1 shared paper)
- Journals
- Natural Computing (1 paper)Proceedings of the VLDB Endowment (1 paper)Lecture notes in computer science (1 paper)Proceedings of the 2022 International Conference on Management of Data (1 paper)IEEE Data(base) Engineering Bulletin (1 paper)
- Partner nations
- United StatesAustria
In The Last Decade
Doris Xin
11 papers receiving 326 citations
Peers
Comparison fields: 5 of 75
- Health Informatics 11
- Information Systems and Management 49
- Artificial Intelligence 186
- Computer Science Applications 19
- Management Science and Operations Research 42
Countries citing papers authored by Doris Xin
This map shows the geographic impact of Doris Xin'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 Doris Xin with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Doris Xin more than expected).
Fields of papers citing papers by Doris Xin
This network shows the impact of papers produced by Doris Xin. 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 Doris Xin. The network helps show where Doris Xin may publish in the future.
Co-authors
The 18 scholars most cited alongside Doris Xin, 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 | 2018 | 86 | |
| 2 | 2021 | 71 | |
| 3 | 2014 | 54 | |
| 4 | 2021 | 41 | |
| 5 | 2018 | 37 | |
| 6 | A Human-in-the-loop Perspective on AutoML: Milestones and the Road Ahead. | 2019 | 27 |
| 7 | 2013 | 11 | |
| 8 | 2014 | 7 | |
| 9 | 2017 | 5 | |
| 10 | 2018 | 4 | |
| 11 | 2022 | 2 |
About Doris Xin
Doris Xin is a scholar working on Artificial Intelligence, Information Systems, Information Systems and Management, Management Science and Operations Research and Computer Networks and Communications, having authored 11 papers that have together received 345 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (4 papers), Scientific Computing and Data Management (3 papers), Advanced Bandit Algorithms Research (3 papers), Data Stream Mining Techniques (2 papers), Machine Learning and Algorithms (2 papers), Advanced Graph Neural Networks (2 papers), Modular Robots and Swarm Intelligence (2 papers) and DNA and Biological Computing (2 papers). The work is most often cited by research in Health Informatics (11 citations), Information Systems and Management (49 citations), Artificial Intelligence (186 citations), Computer Science Applications (19 citations) and Management Science and Operations Research (42 citations). Doris Xin has collaborated with scholars based in United States and Austria. Frequent co-authors include Aditya Parameswaran, Stephen Macke, Niloufar Salehi, Deepak Agarwal, Bo Long, Jonathan Traupman, Hui Miao, Neoklis Polyzotis, Damien Woods and Angela Lee. Their work appears in journals such as Natural Computing, Proceedings of the VLDB Endowment, Lecture notes in computer science, Proceedings of the 2022 International Conference on Management of Data and IEEE Data(base) Engineering Bulletin.
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.