Ga Wu
Impact in
- Information Systems top 5%
- Recommender Systems and Techniques
- Artificial Intelligence top 10%
- Topic Modeling
- Advanced Graph Neural Networks
- Sentiment Analysis and Opinion Mining
- Advanced Text Analysis Techniques
- Speech and dialogue systems
Papers in
-
- Topic Modeling 5
- Sentiment Analysis and Opinion Mining 4
- Data Stream Mining Techniques 3
- AI-based Problem Solving and Planning 2
- Adversarial Robustness in Machine Learning 2
-
- Recommender Systems and Techniques 9
- Co-authors
- Scott Sanner (16 shared papers)Kai Luo (5 shared papers)Hojin Yang (3 shared papers)Maksims Volkovs (2 shared papers)Himanshu Rai (2 shared papers)Harold Soh (1 shared paper)Masoud Hashemi (1 shared paper)Mohamed Reda Bouadjenek (2 shared papers)
- Journals
- ACM Transactions on the Web (1 paper)Machine Learning (1 paper)Proceedings of the AAAI Conference on Artificial Intelligence (2 papers)arXiv (Cornell University) (1 paper)Neural Information Processing Systems (1 paper)
In The Last Decade
Ga Wu
16 papers receiving 198 citations
Peers
Comparison fields: 5 of 45
- Information Systems 119
- Artificial Intelligence 150
- Management Science and Operations Research 35
- Computer Vision and Pattern Recognition 39
- Signal Processing 15
Countries citing papers authored by Ga Wu
This map shows the geographic impact of Ga Wu'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 Ga Wu with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ga Wu more than expected).
Fields of papers citing papers by Ga Wu
This network shows the impact of papers produced by Ga Wu. 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 Ga Wu. The network helps show where Ga Wu may publish in the future.
Co-authors
The 13 scholars most cited alongside Ga Wu, 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 | 29 | |
| 2 | 2019 | 26 | |
| 3 | 2020 | 25 | |
| 4 | 2020 | 25 | |
| 5 | 2022 | 21 | |
| 6 | 2017 | 20 | |
| 7 | 2018 | 20 | |
| 8 | 2022 | 9 | |
| 9 | 2020 | 9 | |
| 10 | 2019 | 8 | |
| 11 | 2015 | 3 | |
| 12 | 2021 | 3 | |
| 13 | Scalable Nonlinear Planning with Deep Neural Network Learned Transition Models. | 2019 | 2 |
| 14 | 2022 | 2 | |
| 15 | 2022 | 1 | |
| 16 | Representer Point Selection via Local Jacobian Expansion for Post-hoc Classifier Explanation of Deep Neural Networks and Ensemble Models | 2021 | 1 |
| 17 | 2020 | 0 |
About Ga Wu
Ga Wu is a scholar working on Artificial Intelligence, Information Systems, Management Science and Operations Research, Computer Vision and Pattern Recognition and Ocean Engineering, having authored 17 papers that have together received 204 indexed citations. Recurring topics across this work include Recommender Systems and Techniques (9 papers), Topic Modeling (5 papers), Sentiment Analysis and Opinion Mining (4 papers), Advanced Bandit Algorithms Research (4 papers), Data Stream Mining Techniques (3 papers), AI-based Problem Solving and Planning (2 papers), Adversarial Robustness in Machine Learning (2 papers) and Music and Audio Processing (2 papers). The work is most often cited by research in Information Systems (119 citations), Artificial Intelligence (150 citations), Management Science and Operations Research (35 citations), Computer Vision and Pattern Recognition (39 citations) and Signal Processing (15 citations). Ga Wu has collaborated with scholars based in Canada, Austria and Australia. Frequent co-authors include Scott Sanner, Kai Luo, Hojin Yang, Maksims Volkovs, Himanshu Rai, Harold Soh, Masoud Hashemi, Mohamed Reda Bouadjenek, Yu Zhou and Yichao Lu. Their work appears in journals such as ACM Transactions on the Web, Machine Learning, Proceedings of the AAAI Conference on Artificial Intelligence, arXiv (Cornell University) and Neural Information Processing Systems.
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.