Brian Bullins

15 papers and 144 indexed citations i.

About

Brian Bullins is a scholar working on Artificial Intelligence, Computational Mechanics and Computational Theory and Mathematics. According to data from OpenAlex, Brian Bullins has authored 15 papers receiving a total of 144 indexed citations (citations by other indexed papers that have themselves been cited), including 13 papers in Artificial Intelligence, 10 papers in Computational Mechanics and 6 papers in Computational Theory and Mathematics. Recurrent topics in Brian Bullins’s work include Stochastic Gradient Optimization Techniques (11 papers), Sparse and Compressive Sensing Techniques (10 papers) and Machine Learning and Algorithms (5 papers). Brian Bullins is often cited by papers focused on Stochastic Gradient Optimization Techniques (11 papers), Sparse and Compressive Sensing Techniques (10 papers) and Machine Learning and Algorithms (5 papers). Brian Bullins collaborates with scholars based in United States, France and Israel. Brian Bullins's co-authors include Naman Agarwal, Elad Hazan, Tengyu Ma, Zeyuan Allen-Zhu, Nicolas Boumal, Coralia Cartis, Zeyuan Allen Zhu, Marianna Bolla, Katalin Friedl and Sushant Sachdeva and has published in prestigious journals such as Mathematical Programming, Journal of Machine Learning Research and SIAM Journal on Optimization.

In The Last Decade

Co-authorship network of co-authors of Brian Bullins i

Fields of papers citing papers by Brian Bullins

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Countries citing papers authored by Brian Bullins

Since Specialization
Citations

This map shows the geographic impact of Brian Bullins'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 Brian Bullins with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Brian Bullins 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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2025