James Sharpnack

1.1k total citations
32 papers, 405 citations indexed

About

James Sharpnack is a scholar working on Artificial Intelligence, Statistics and Probability and Molecular Biology. According to data from OpenAlex, James Sharpnack has authored 32 papers receiving a total of 405 indexed citations (citations by other indexed papers that have themselves been cited), including 14 papers in Artificial Intelligence, 8 papers in Statistics and Probability and 5 papers in Molecular Biology. Recurrent topics in James Sharpnack's work include Statistical Methods and Inference (8 papers), Anomaly Detection Techniques and Applications (5 papers) and Bayesian Methods and Mixture Models (4 papers). James Sharpnack is often cited by papers focused on Statistical Methods and Inference (8 papers), Anomaly Detection Techniques and Applications (5 papers) and Bayesian Methods and Mixture Models (4 papers). James Sharpnack collaborates with scholars based in United States, France and Canada. James Sharpnack's co-authors include Liwei Wu, Cho‐Jui Hsieh, Shuqing Li, Aarti Singh, Alessandro Rinaldo, Susan Handy, Dillon T. Fitch, Ryan J. Tibshirani, Akshay Krishnamurthy and Oscar Hernán Madrid Padilla and has published in prestigious journals such as Proceedings of the National Academy of Sciences, SHILAP Revista de lepidopterología and PLoS ONE.

In The Last Decade

James Sharpnack

31 papers receiving 386 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name	h						Papers	Cites
James Sharpnack United States	11	181	145	64	50	43	32	405
Utkarsh Upadhyay India	9	143 0.8×	74 0.5×	59 0.9×	36 0.7×	81 1.9×	24	523
Peter Kairouz United States	15	892 4.9×	90 0.6×	87 1.4×	27 0.5×	34 0.8×	47	1.1k
Xin Guo China	15	190 1.0×	37 0.3×	63 1.0×	27 0.5×	6 0.1×	77	531
Kun-Ta Chuang Taiwan	14	195 1.1×	140 1.0×	109 1.7×	31 0.6×	14 0.3×	53	492
Ananda Theertha Suresh United States	15	624 3.4×	56 0.4×	81 1.3×	15 0.3×	6 0.1×	45	744
Sinead A. Williamson United States	11	258 1.4×	31 0.2×	48 0.8×	11 0.2×	9 0.2×	29	393
Yasuko Matsubara Japan	11	328 1.8×	81 0.6×	59 0.9×	54 1.1×	41 1.0×	40	669
Muhammad Tanveer Hussain Pakistan	8	69 0.4×	59 0.4×	44 0.7×	25 0.5×	5 0.1×	27	260
Sunita Garhwal India	8	253 1.4×	55 0.4×	40 0.6×	20 0.4×	6 0.1×	26	482
David Rincón Spain	13	50 0.3×	87 0.6×	49 0.8×	41 0.8×	19 0.4×	51	619

Countries citing papers authored by James Sharpnack

Since Specialization

Citations

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

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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-authorship network of co-authors of James Sharpnack

This figure shows the co-authorship network connecting the top 25 collaborators of James Sharpnack. A scholar is included among the top collaborators of James Sharpnack 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 James Sharpnack. James Sharpnack is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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20 of 20 papers shown

Sharpnack, James, et al.. (2025). Improving lung cancer diagnosis and survival prediction with deep learning and CT imaging. PLoS ONE. 20(6). e0323174–e0323174. 2 indexed citations

Sharpnack, James, et al.. (2024). Exponential family trend filtering on lattices. Electronic Journal of Statistics. 18(1). 1 indexed citations

Sharpnack, James, et al.. (2023). Comparative performance analysis of three machine learning algorithms applied to sensor data registered by a leg-attached accelerometer to predict metritis events in dairy cattle. SHILAP Revista de lepidopterología. 4. 4 indexed citations

Jones, Tucker, et al.. (2023). Optimizing machine learning methods to discover strong gravitational lenses in the deep lens survey. Monthly Notices of the Royal Astronomical Society. 524(4). 5368–5390. 3 indexed citations

Sharpnack, James, et al.. (2023). Impact of sensor data pre-processing strategies and selection of machine learning algorithm on the prediction of metritis events in dairy cattle. Preventive Veterinary Medicine. 215. 105903–105903. 10 indexed citations

Pollock, Brad H., et al.. (2022). The impact of COVID-19 vaccination on California’s return to normalcy. PLoS ONE. 17(5). e0264195–e0264195. 4 indexed citations

McDonald, Daniel J., Jacob Bien, Alden Green, et al.. (2021). Can auxiliary indicators improve COVID-19 forecasting and hotspot prediction?. Proceedings of the National Academy of Sciences. 118(51). 22 indexed citations

Ingram, Paul B., et al.. (2021). The Influence of Service Era: Comparing Personality Assessment Inventory (PAI) Scale Scores Within a Posttraumatic Stress Disorder Treatment Clinic (PCT). Journal of Clinical Psychology in Medical Settings. 29(3). 624–635. 2 indexed citations

Wu, Liwei, Shuqing Li, Cho‐Jui Hsieh, & James Sharpnack. (2020). SSE-PT: Sequential Recommendation Via Personalized Transformer. 328–337. 145 indexed citations

10.

Wu, Liwei, Shuqing Li, Cho‐Jui Hsieh, & James Sharpnack. (2019). Stochastic Shared Embeddings: Data-driven Regularization of Embedding Layers. arXiv (Cornell University). 32. 24–34. 3 indexed citations

11.

Wu, Liwei, Cho‐Jui Hsieh, & James Sharpnack. (2018). SQL-Rank: A Listwise Approach to Collaborative Ranking. International Conference on Machine Learning. 5315–5324. 3 indexed citations

12.

Sharpnack, James. (2018). Learning Patterns for Detection with Multiscale Scan Statistics. Conference on Learning Theory. 950–969. 1 indexed citations

13.

Sharpnack, James, Alessandro Rinaldo, & Aarti Singh. (2018). Sparsistency of the Edge Lasso over Graphs. Figshare. 22. 1028–1036. 11 indexed citations

14.

Sharpnack, James & Aarti Singh. (2018). Identifying graph-structured activation patterns in networks. Figshare. 23. 2137–2145. 1 indexed citations

15.

Srivastava, Arunima, Ferdinando Cerciello, Simona G. Codreanu, et al.. (2018). Proteogenomic Analysis of Surgically Resected Lung Adenocarcinoma. Journal of Thoracic Oncology. 13(10). 1519–1529. 19 indexed citations

16.

Padilla, Oscar Hernán Madrid, James Sharpnack, & James G. Scott. (2017). The DFS fused lasso: linear-time denoising over general graphs. Journal of Machine Learning Research. 18(1). 6410–6445. 9 indexed citations

17.

Wang, Yu-Xiang, et al.. (2017). Higher-Order Total Variation Classes on Grids: Minimax Theory and Trend Filtering Methods.. Neural Information Processing Systems. 30. 5800–5810. 5 indexed citations

18.

Lin, Kevin, James Sharpnack, Alessandro Rinaldo, & Ryan J. Tibshirani. (2017). A Sharp Error Analysis for the Fused Lasso, with Application to Approximate Changepoint Screening. Neural Information Processing Systems. 30. 6884–6893. 19 indexed citations

19.

Padilla, Oscar Hernán Madrid, James G. Scott, James Sharpnack, & Ryan J. Tibshirani. (2016). The DFS fused lasso: nearly optimal linear-time denoising over graphs and trees. arXiv (Cornell University). 1 indexed citations

20.

Wang, Yuxiang, James Sharpnack, Alexander J. Smola, & Ryan J. Tibshirani. (2015). Trend Filtering on Graphs. Journal of Machine Learning Research. 17(1). 1042–1050. 10 indexed citations

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.

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