Karan Goel
Impact in
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- Topic Modeling
- Natural Language Processing Techniques
- Anomaly Detection Techniques and Applications
- Machine Learning and Algorithms
- Machine Learning and Data Classification
Papers in
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- Machine Learning and Data Classification 3
- Data Stream Mining Techniques 2
- Natural Language Processing Techniques 2
- Explainable Artificial Intelligence (XAI) 2
- Topic Modeling 2
- Anomaly Detection Techniques and Applications 2
- Surgery 1
- Co-authors
- Emma Brunskill (1 shared paper)Laurel Orr (2 shared papers)Xiao Ling (1 shared paper)Nimit S. Sohoni (1 shared paper)Christopher Ré (2 shared papers)Kayvon Fatahalian (1 shared paper)Christopher Ré (3 shared papers)Albert Gu (3 shared papers)
- Journals
- Clinical and Translational Gastroenterology (1 paper)Proceedings of the VLDB Endowment (1 paper)International Conference on Learning Representations (1 paper)arXiv (Cornell University) (3 papers)
- Partner nations
- United States
In The Last Decade
Karan Goel
9 papers receiving 34 citations
Peers
Comparison fields: 5 of 22
- Artificial Intelligence 21
- Gastroenterology 3
- Computer Vision and Pattern Recognition 9
- Signal Processing 4
- Information Systems and Management 2
Countries citing papers authored by Karan Goel
This map shows the geographic impact of Karan Goel'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 Karan Goel with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Karan Goel more than expected).
Fields of papers citing papers by Karan Goel
This network shows the impact of papers produced by Karan Goel. 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 Karan Goel. The network helps show where Karan Goel may publish in the future.
Co-authors
The 25 scholars most cited alongside Karan Goel, 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 | 2021 | 10 | |
| 2 | Learning Procedural Abstractions and Evaluating Discrete Latent Temporal Structure | 2018 | 8 |
| 3 | 2021 | 6 | |
| 4 | Model Patching: Closing the Subgroup Performance Gap with Data Augmentation | 2021 | 3 |
| 5 | 2023 | 3 | |
| 6 | An Efficient Hardwired Realization of Embedded Neural Controller on System-On-Programmable-Chip (SOPC) | 2014 | 1 |
| 7 | 2021 | 1 | |
| 8 | 2024 | 1 | |
| 9 | 2021 | 1 | |
| 10 | 2022 | 0 |
About Karan Goel
Karan Goel is a scholar working on Artificial Intelligence, Surgery, Computer Networks and Communications, Information Systems and Computer Vision and Pattern Recognition, having authored 10 papers that have together received 34 indexed citations. Recurring topics across this work include Machine Learning and Data Classification (3 papers), Data Stream Mining Techniques (2 papers), Natural Language Processing Techniques (2 papers), Explainable Artificial Intelligence (XAI) (2 papers), Topic Modeling (2 papers), Anomaly Detection Techniques and Applications (2 papers), Digital Imaging for Blood Diseases (1 paper) and Mobile Crowdsensing and Crowdsourcing (1 paper). The work is most often cited by research in Artificial Intelligence (21 citations), Gastroenterology (3 citations), Computer Vision and Pattern Recognition (9 citations), Signal Processing (4 citations) and Information Systems and Management (2 citations). Karan Goel has collaborated with scholars based in United States. Frequent co-authors include Emma Brunskill, Laurel Orr, Xiao Ling, Nimit S. Sohoni, Christopher Ré, Kayvon Fatahalian, Christopher Ré, Albert Gu, Yixuan Li and Chris Ré. Their work appears in journals such as Clinical and Translational Gastroenterology, Proceedings of the VLDB Endowment, International Conference on Learning Representations 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.