Noah Siegel

About

Noah Siegel is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Industrial relations. According to data from OpenAlex, Noah Siegel has authored 4 papers receiving a total of 22 indexed citations (citations by other indexed papers that have themselves been cited), including 3 papers in Artificial Intelligence, 1 paper in Computational Theory and Mathematics and 0 papers in Industrial relations. Recurrent topics in Noah Siegel’s work include Adversarial Robustness in Machine Learning (3 papers), Reinforcement Learning in Robotics (3 papers) and Machine Learning and Data Classification (1 paper). Noah Siegel is often cited by papers focused on Adversarial Robustness in Machine Learning (3 papers), Reinforcement Learning in Robotics (3 papers) and Machine Learning and Data Classification (1 paper). Noah Siegel collaborates with scholars based in United States, United Kingdom and Germany. Noah Siegel's co-authors include Michael Neunert, Nicolas Heess, Abbas Abdolmaleki, Thomas Lampe, Roland Hafner, Martin Riedmiller, Jost Tobias Springenberg, Markus Wulfmeier, Felix Berkenkamp and Dushyant Rao and has published in prestigious journals such as arXiv (Cornell University).

In The Last Decade

Co-authorship network of co-authors of Noah Siegel i

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Fields of papers citing papers by Noah Siegel

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Countries citing papers authored by Noah Siegel

Since Specialization

Citations

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

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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