James Sharpnack
Impact in
- Information Systems top 5%
- Recommender Systems and Techniques
- Transportation top 10%
Papers in
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- Anomaly Detection Techniques and Applications 5
- Bayesian Methods and Mixture Models 4
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- Statistical Methods and Inference 8
- Co-authors
- Cho‐Jui Hsieh (4 shared papers)Liwei Wu (4 shared papers)Shuqing Li (1 shared paper)Aarti Singh (5 shared papers)Alessandro Rinaldo (3 shared papers)Susan Handy (1 shared paper)Dillon T. Fitch (1 shared paper)Ryan J. Tibshirani (5 shared papers)
- Journals
- PLoS ONE (2 papers)Journal of Machine Learning Research (2 papers)Electronic Journal of Statistics (1 paper)Monthly Notices of the Royal Astronomical Society (1 paper)ACS ES&T Water (1 paper)
- Partner nations
- United StatesCanadaFrance
In The Last Decade
James Sharpnack
31 papers receiving 404 citations
Peers
Comparison fields: 5 of 83
- Information Systems 152
- Transportation 42
- Artificial Intelligence 185
- Statistics and Probability 39
- Modeling and Simulation 20
Countries citing papers authored by James Sharpnack
This map shows the geographic impact of James Sharpnack'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 James Sharpnack with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites James Sharpnack more than expected).
Fields of papers citing papers by James Sharpnack
This network shows the impact of papers produced by James Sharpnack. 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 James Sharpnack. The network helps show where James Sharpnack may publish in the future.
Co-authors
The 25 scholars most cited alongside James Sharpnack, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 32 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2020 | 154 | |
| 2 | 2020 | 40 | |
| 3 | 2013 | 26 | |
| 4 | 2021 | 22 | |
| 5 | 2018 | 21 | |
| 6 | A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening | 2017 | 19 |
| 7 | 2015 | 19 | |
| 8 | 2022 | 11 | |
| 9 | 2023 | 11 | |
| 10 | 2018 | 11 | |
| 11 | The DFS fused lasso: linear-time denoising over general graphs | 2017 | 10 |
| 12 | 2019 | 10 | |
| 13 | 2017 | 10 | |
| 14 | 2015 | 9 | |
| 15 | 2022 | 7 | |
| 16 | 2013 | 7 | |
| 17 | Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods. | 2017 | 5 |
| 18 | 2023 | 4 | |
| 19 | 2022 | 4 | |
| 20 | Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers | 2019 | 3 |
About James Sharpnack
James Sharpnack is a scholar working on Artificial Intelligence, Statistics and Probability, Molecular Biology, Computer Vision and Pattern Recognition and Management Science and Operations Research, having authored 32 papers that have together received 423 indexed citations. Recurring topics across this work include Statistical Methods and Inference (8 papers), Anomaly Detection Techniques and Applications (5 papers), Bayesian Methods and Mixture Models (4 papers), Image and Signal Denoising Methods (4 papers), COVID-19 epidemiological studies (3 papers), Sparse and Compressive Sensing Techniques (3 papers), Data-Driven Disease Surveillance (3 papers) and Recommender Systems and Techniques (3 papers). The work is most often cited by research in Information Systems (152 citations), Transportation (42 citations), Artificial Intelligence (185 citations), Statistics and Probability (39 citations) and Modeling and Simulation (20 citations). James Sharpnack has collaborated with scholars based in United States, Canada and France. Frequent co-authors include Cho‐Jui Hsieh, Liwei Wu, Shuqing Li, Aarti Singh, Alessandro Rinaldo, Susan Handy, Dillon T. Fitch, Ryan J. Tibshirani, Akshay Krishnamurthy and Oscar Hernán Madrid Padilla. Their work appears in journals such as PLoS ONE, Journal of Machine Learning Research, Electronic Journal of Statistics, Monthly Notices of the Royal Astronomical Society and ACS ES&T Water.
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.