Matthias Seeger

About

Matthias Seeger is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Management Science and Operations Research. According to data from OpenAlex, Matthias Seeger has authored 44 papers receiving a total of 4.4k indexed citations (citations by other indexed papers that have themselves been cited), including 37 papers in Artificial Intelligence, 12 papers in Computational Theory and Mathematics and 8 papers in Management Science and Operations Research. Recurrent topics in Matthias Seeger’s work include Gaussian Processes and Bayesian Inference (29 papers), Advanced Multi-Objective Optimization Algorithms (12 papers) and Machine Learning and Algorithms (10 papers). Matthias Seeger is often cited by papers focused on Gaussian Processes and Bayesian Inference (29 papers), Advanced Multi-Objective Optimization Algorithms (12 papers) and Machine Learning and Algorithms (10 papers). Matthias Seeger collaborates with scholars based in Germany, United States and Switzerland. Matthias Seeger's co-authors include Christopher K. I. Williams, Sham M. Kakade, Niranjan Srinivas, Andreas Krause, Neil D. Lawrence, Duy Nguyen-Tuong, Jan Peters, Ralf Herbrich, Hannes Nickisch and Yuyang Wang and has published in prestigious journals such as IEEE Transactions on Information Theory, Magnetic Resonance in Medicine and IEEE Signal Processing Magazine.

In The Last Decade

Co-authorship network of co-authors of Matthias Seeger i

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Fields of papers citing papers by Matthias Seeger

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Countries citing papers authored by Matthias Seeger

Since Specialization

Citations

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