Philip Pham
Impact in
- Artificial Intelligence top 10%
- Topic Modeling
- Natural Language Processing Techniques
- Advanced Text Analysis Techniques
- Text and Document Classification Technologies
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- Multimodal Machine Learning Applications
Papers in
-
- Topic Modeling 3
- Adversarial Robustness in Machine Learning 2
- Anomaly Detection Techniques and Applications 2
- Natural Language Processing Techniques 2
- Explainable Artificial Intelligence (XAI) 2
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- Nonlinear Dynamics and Pattern Formation 1
- Co-authors
- Robin Pemantle (1 shared paper)Diana C. Mutz (1 shared paper)Joshua Ainslie (2 shared papers)Anirudh Ravula (1 shared paper)Yang Li (1 shared paper)Vaclav Cvicek (1 shared paper)Santiago Ontañón (1 shared paper)Sumit Sanghai (1 shared paper)
- Journals
- International Journal for Numerical Methods in Biomedical Engineering (1 paper)The American Statistician (1 paper)Neural Information Processing Systems (1 paper)
- Partner nations
- United StatesSwitzerlandNetherlands
In The Last Decade
Philip Pham
8 papers receiving 301 citations
Peers
Comparison fields: 5 of 78
- Artificial Intelligence 163
- Computer Vision and Pattern Recognition 57
- General Decision Sciences 5
- Modeling and Simulation 9
- Safety Research 16
Countries citing papers authored by Philip Pham
This map shows the geographic impact of Philip Pham'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 Philip Pham with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Philip Pham more than expected).
Fields of papers citing papers by Philip Pham
This network shows the impact of papers produced by Philip Pham. 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 Philip Pham. The network helps show where Philip Pham may publish in the future.
Co-authors
The 25 scholars most cited alongside Philip Pham, 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 | 2020 | 151 | |
| 2 | 2017 | 120 | |
| 3 | Long Range Arena : A Benchmark for Efficient Transformers | 2021 | 15 |
| 4 | 2011 | 8 | |
| 5 | 2021 | 8 | |
| 6 | 2021 | 7 | |
| 7 | Fair Hierarchical Clustering | 2020 | 1 |
| 8 | 2020 | 1 |
About Philip Pham
Philip Pham is a scholar working on Artificial Intelligence, Computer Networks and Communications, Oceanography, Computer Vision and Pattern Recognition and Atomic and Molecular Physics, and Optics, having authored 8 papers that have together received 311 indexed citations. Recurring topics across this work include Topic Modeling (3 papers), Adversarial Robustness in Machine Learning (2 papers), Anomaly Detection Techniques and Applications (2 papers), Natural Language Processing Techniques (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Nonlinear Dynamics and Pattern Formation (1 paper), Advanced Causal Inference Techniques (1 paper) and Spectroscopy and Quantum Chemical Studies (1 paper). The work is most often cited by research in Artificial Intelligence (163 citations), Computer Vision and Pattern Recognition (57 citations), General Decision Sciences (5 citations), Modeling and Simulation (9 citations) and Safety Research (16 citations). Philip Pham has collaborated with scholars based in United States, Switzerland and Netherlands. Frequent co-authors include Robin Pemantle, Diana C. Mutz, Joshua Ainslie, Anirudh Ravula, Yang Li, Vaclav Cvicek, Santiago Ontañón, Sumit Sanghai, Qifan Wang and Zachary Fisher. Their work appears in journals such as International Journal for Numerical Methods in Biomedical Engineering, The American Statistician 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.