Thomas Verma
Impact in
- Artificial Intelligence top 1%
- Bayesian Modeling and Causal Inference
- AI-based Problem Solving and Planning
- Logic, Reasoning, and Knowledge
- Statistics and Probability top 2%
- Advanced Causal Inference Techniques
Papers in
-
- Bayesian Modeling and Causal Inference 8
- Logic, Reasoning, and Knowledge 4
- Cognitive Science and Mapping 2
- Machine Learning and Algorithms 1
- AI-based Problem Solving and Planning 1
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- Data Management and Algorithms 2
- Journals
- Networks (1 paper)Statistics and Computing (1 paper)Machine intelligence and pattern recognition (2 papers)Studies in logic and the foundations of mathematics (1 paper)National Conference on Artificial Intelligence (1 paper)
- Partner nations
- United States
In The Last Decade
Thomas Verma
9 papers receiving 1.2k citations
Peers
Comparison fields: 5 of 119
- Artificial Intelligence 1.0k
- Statistics and Probability 220
- Management Science and Operations Research 275
- Signal Processing 148
- History and Philosophy of Science 51
Countries citing papers authored by Thomas Verma
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).
Fields of papers citing papers by Thomas Verma
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.
Co-authors
The 3 scholars most cited alongside Thomas Verma, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | A Theory of Inferred Causation. | 1991 | 392 |
| 2 | 1990 | 339 | |
| 3 | 1990 | 242 | |
| 4 | 1995 | 129 | |
| 5 | 1990 | 123 | |
| 6 | 1987 | 86 | |
| 7 | 1992 | 30 | |
| 8 | 1992 | 7 | |
| 9 | 2018 | 1 |
About Thomas Verma
Thomas Verma is a scholar working on Artificial Intelligence, Signal Processing, Management Science and Operations Research, Statistics and Probability and Infectious Diseases, having authored 9 papers that have together received 1.3k indexed citations. Recurring topics across this work include Bayesian Modeling and Causal Inference (8 papers), Logic, Reasoning, and Knowledge (4 papers), Data Management and Algorithms (2 papers), Cognitive Science and Mapping (2 papers), Machine Learning and Algorithms (1 paper), AI-based Problem Solving and Planning (1 paper), Multi-Criteria Decision Making (1 paper) and Statistical Methods and Inference (1 paper). The work is most often cited by research in Artificial Intelligence (1.0k citations), Statistics and Probability (220 citations), Management Science and Operations Research (275 citations), Signal Processing (148 citations) and History and Philosophy of Science (51 citations). Thomas Verma has collaborated with scholars based in United States. Frequent co-authors include Judea Pearl, Dan Geiger and Peter Spirtes. Their work appears in journals such as Networks, Statistics and Computing, Machine intelligence and pattern recognition, Studies in logic and the foundations of mathematics and National Conference on Artificial Intelligence.
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.