Kevin Scaman

535 total citations
9 papers, 105 citations indexed

About

Kevin Scaman is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Computational Mechanics. According to data from OpenAlex, Kevin Scaman has authored 9 papers receiving a total of 105 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 3 papers in Statistical and Nonlinear Physics and 3 papers in Computational Mechanics. Recurrent topics in Kevin Scaman's work include Sparse and Compressive Sensing Techniques (3 papers), Complex Network Analysis Techniques (3 papers) and Point processes and geometric inequalities (2 papers). Kevin Scaman is often cited by papers focused on Sparse and Compressive Sensing Techniques (3 papers), Complex Network Analysis Techniques (3 papers) and Point processes and geometric inequalities (2 papers). Kevin Scaman collaborates with scholars based in France, China and India. Kevin Scaman's co-authors include Nicolas Vayatis and Cédric Malherbe and has published in prestigious journals such as IEEE Transactions on Network Science and Engineering, arXiv (Cornell University) and Neural Information Processing Systems.

In The Last Decade

Kevin Scaman

9 papers receiving 104 citations

Peers

Kevin Scaman

AI

SNP

CVPR

CM

MB

Elena Ceci Italy

Jason M. Klusowski United States

Motonobu Kanagawa Japan

Roberto Notari Italy

Kevin Broughan New Zealand

Jason M. Altschuler United States

Nicolas Flammarion United States

Dima Kuzmin United States

Guillaume Cleuziou France

Justin Whitehouse United States

Elena Ceci Italy

Kevin Scaman

63 ×1.1

AI

31 ×1.2

SNP

5 ×0.3

CVPR

9 ×0.7

CM

8 ×0.7

MB

Citations per year, relative to Kevin Scaman Kevin Scaman (= 1×) peers Elena Ceci

Countries citing papers authored by Kevin Scaman

Since Specialization

Citations

This map shows the geographic impact of Kevin Scaman'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 Kevin Scaman with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Kevin Scaman more than expected).

Fields of papers citing papers by Kevin Scaman

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Kevin Scaman. 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 Kevin Scaman. The network helps show where Kevin Scaman may publish in the future.

Co-authorship network of co-authors of Kevin Scaman

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

All Works

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9 of 9 papers shown

1.

Scaman, Kevin & Cédric Malherbe. (2020). Robustness Analysis of Non-Convex Stochastic Gradient Descent using Biased Expectations. Neural Information Processing Systems. 33. 16377–16387. 5 indexed citations

2.

Scaman, Kevin, et al.. (2020). A Simple and Efficient Smoothing Method for Faster Optimization and Local Exploration. Neural Information Processing Systems. 33. 6503–6513. 3 indexed citations

3.

Scaman, Kevin, et al.. (2019). Theoretical Limits of Pipeline Parallel Optimization and Application to Distributed Deep Learning. arXiv (Cornell University). 32. 12371–12380. 1 indexed citations

4.

Scaman, Kevin, et al.. (2018). Lipschitz regularity of deep neural networks: analysis and efficient estimation. Neural Information Processing Systems. 31. 3835–3844. 52 indexed citations

5.

Scaman, Kevin, et al.. (2017). Multivariate Hawkes Processes for Large-Scale Inference. Proceedings of the AAAI Conference on Artificial Intelligence. 31(1). 17 indexed citations

6.

Scaman, Kevin, et al.. (2016). Suppressing Epidemics in Networks Using Priority Planning. IEEE Transactions on Network Science and Engineering. 3(4). 271–285. 16 indexed citations

7.

Scaman, Kevin, et al.. (2016). Multivariate Hawkes Processes for Large-scale Inference. arXiv (Cornell University). 2168–2174. 4 indexed citations

8.

Scaman, Kevin, et al.. (2015). A Greedy Approach for Dynamic Control of Diffusion Processes in Networks. 7. 652–659. 6 indexed citations

9.

Scaman, Kevin, et al.. (2014). Dynamic Treatment Allocation for Epidemic Control in Arbitrary Networks. 1 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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