Chris Cundy
Impact in
-
- Geographic Information Systems Studies
-
- Human Mobility and Location-Based Analysis
Papers in
-
- Neural Networks and Applications 2
- Machine Learning and Data Classification 2
- Bayesian Modeling and Causal Inference 1
- Co-authors
- Gengchen Mai (3 shared papers)Yingjie Hu (3 shared papers)Ni Lao (3 shared papers)Kristy Choi (2 shared papers)Wei Liu (1 shared paper)Ryan Zhenqi Zhou (1 shared paper)Kenneth Joseph (1 shared paper)Stefano Ermon (3 shared papers)
- Journals
- ACM Transactions on Spatial Algorithms and Systems (1 paper)International Journal of Multiphase Flow (1 paper)Uncertainty in Artificial Intelligence (1 paper)International Journal of Geographical Information Systems (1 paper)arXiv (Cornell University) (2 papers)
- Partner nations
- United StatesSingaporeUnited Kingdom
In The Last Decade
Chris Cundy
7 papers receiving 208 citations
Chris Cundy's Hit Papers
Peers
Comparison fields: 5 of 56
- Geography, Planning and Development 68
- Transportation 23
- Health Informatics 4
- Signal Processing 31
- Artificial Intelligence 84
Countries citing papers authored by Chris Cundy
This map shows the geographic impact of Chris Cundy'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 Chris Cundy with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Chris Cundy more than expected).
Fields of papers citing papers by Chris Cundy
This network shows the impact of papers produced by Chris Cundy. 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 Chris Cundy. The network helps show where Chris Cundy may publish in the future.
Co-authors
The 22 scholars most cited alongside Chris Cundy, 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 | Geo-knowledge-guided GPT models improve the extraction of location descriptions from disaster-related social media messages Hit paper breakdown → | 2023 | 100 |
| 2 | On the Opportunities and Challenges of Foundation Models for GeoAI (Vision Paper) Hit paper breakdown → | 2024 | 55 |
| 3 | 2022 | 42 | |
| 4 | Parallelizing Linear Recurrent Neural Nets Over Sequence Length. | 2018 | 8 |
| 5 | 2024 | 7 | |
| 6 | 2021 | 2 | |
| 7 | Flexible Approximate Inference via Stratified Normalizing Flows. | 2020 | 1 |
About Chris Cundy
Chris Cundy is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Geography, Planning and Development, Communication and Control and Systems Engineering, having authored 7 papers that have together received 215 indexed citations. Recurring topics across this work include Geographic Information Systems Studies (2 papers), Neural Networks and Applications (2 papers), Machine Learning and Data Classification (2 papers), Fluid Dynamics and Heat Transfer (1 paper), Geological Modeling and Analysis (1 paper), Bayesian Modeling and Causal Inference (1 paper), Disaster Management and Resilience (1 paper) and Public Relations and Crisis Communication (1 paper). The work is most often cited by research in Geography, Planning and Development (68 citations), Transportation (23 citations), Health Informatics (4 citations), Signal Processing (31 citations) and Artificial Intelligence (84 citations). Chris Cundy has collaborated with scholars based in United States, Singapore and United Kingdom. Frequent co-authors include Gengchen Mai, Yingjie Hu, Ni Lao, Kristy Choi, Wei Liu, Ryan Zhenqi Zhou, Kenneth Joseph, Stefano Ermon, Song Gao and Ninghao Liu. Their work appears in journals such as ACM Transactions on Spatial Algorithms and Systems, International Journal of Multiphase Flow, Uncertainty in Artificial Intelligence, International Journal of Geographical Information Systems and arXiv (Cornell University).
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.