Pang Wei Koh
- Modeling and Simulation top 0.5%
- Artificial Intelligence top 2%
- Computer Vision and Pattern Recognition top 2%
- Transportation top 2%
- Epidemiology
- Co-authors
- Emma PiersonJure LeskovecDavid B. GruskySerina ChangBeth RedbirdJaline GerardinZhenghao ChenAndrew Y. Ng
- Topics
- Machine Learning and Data Classification (3 papers)Domain Adaptation and Few-Shot Learning (3 papers)Image and Signal Denoising Methods (2 papers)
- Partner nations
- United StatesSingaporeSwitzerland
In The Last Decade
Pang Wei Koh
27 papers receiving 1.9k citations
Hit Papers
Peers
Comparison fields: 5 of 141
- Modeling and Simulation 613
- Artificial Intelligence 508
- Computer Vision and Pattern Recognition 368
- Transportation 293
- Epidemiology 248
Countries citing papers authored by Pang Wei Koh
This map shows the geographic impact of Pang Wei Koh'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 Pang Wei Koh with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Pang Wei Koh more than expected).
Fields of papers citing papers by Pang Wei Koh
This network shows the impact of papers produced by Pang Wei Koh. 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 Pang Wei Koh. The network helps show where Pang Wei Koh may publish in the future.
Co-authorship network of co-authors of Pang Wei Koh
This figure shows the co-authorship network connecting the top 25 collaborators of Pang Wei Koh. A scholar is included among the top collaborators of Pang Wei Koh based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Pang Wei Koh. Pang Wei Koh is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 5 | |
| 3 | 1 | |
| 4 | 24 | |
| 5 | 1 | |
| 6 | 2 | |
| 7 | 80 | |
| 8 | 19 | |
| 9 | Distributionally Robust Neural Networks | 49 |
| 10 | An Investigation of Why Overparameterization Exacerbates Spurious Correlations | 6 |
| 11 | Mobility network models of COVID-19 explain inequities and inform reopeningbreakdown → | 958 |
| 12 | On the Accuracy of Influence Functions for Measuring Group Effects | 16 |
| 13 | 33 | |
| 14 | 10 | |
| 15 | 35 | |
| 16 | 32 | |
| 17 | Sparse Filtering | 118 |
| 18 | Learning Deep Energy Models | 64 |
| 19 | On Random Weights and Unsupervised Feature Learning | 165 |
| 20 | Tiled convolutional neural networks | 169 |
About Pang Wei Koh
Pang Wei Koh is a scholar working on Health Informatics, Modeling and Simulation and Artificial Intelligence, having authored 30 papers that have together received 1.9k indexed citations. Recurring topics across this work include Machine Learning and Data Classification (3 papers), Domain Adaptation and Few-Shot Learning (3 papers) and Image and Signal Denoising Methods (2 papers). The work is most often cited by research in Modeling and Simulation (613 citations), Transportation (293 citations) and Health Informatics (29 citations). Pang Wei Koh has collaborated with scholars based in United States, Singapore and Switzerland. Frequent co-authors include Emma Pierson, Jure Leskovec, David B. Grusky, Serina Chang, Beth Redbird, Jaline Gerardin, Zhenghao Chen, Andrew Y. Ng, Jiquan Ngiam and Percy Liang. Their work appears in journals such as Nature, Proceedings of the National Academy of Sciences and Nature Medicine.
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.