Thomas Verma

4 papers and 548 indexed citations i.

About

Thomas Verma is a scholar working on Artificial Intelligence, Management Science and Operations Research and Statistics and Probability. According to data from OpenAlex, Thomas Verma has authored 4 papers receiving a total of 548 indexed citations (citations by other indexed papers that have themselves been cited), including 4 papers in Artificial Intelligence, 1 paper in Management Science and Operations Research and 1 paper in Statistics and Probability. Recurrent topics in Thomas Verma’s work include Bayesian Modeling and Causal Inference (3 papers), Logic, Reasoning, and Knowledge (2 papers) and Machine Learning and Algorithms (1 paper). Thomas Verma is often cited by papers focused on Bayesian Modeling and Causal Inference (3 papers), Logic, Reasoning, and Knowledge (2 papers) and Machine Learning and Algorithms (1 paper). Thomas Verma collaborates with scholars based in United States. Thomas Verma's co-authors include Judea Pearl and Dan Geiger and has published in prestigious journals such as Networks, Statistics and Computing and Principles of Knowledge Representation and Reasoning.

In The Last Decade

Co-authorship network of co-authors of Thomas Verma i

Fields of papers citing papers by Thomas Verma

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Countries citing papers authored by Thomas Verma

Since Specialization
Citations

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