Ashwin Srinivasan
- Artificial Intelligence top 2%
- Computational Theory and Mathematics top 1%
- Information Systems top 2%
- Molecular Biology
- Computer Vision and Pattern Recognition top 5%
- Co-authors
- Ross D. KingStephen MuggletonMichael J.E. SternbergStefan KrämerChristoph HelmaDavid PageTirtharaj DashHendrik Blockeel
- Topics
- Logic, Reasoning, and Knowledge (20 papers)Computational Drug Discovery Methods (15 papers)Data Mining Algorithms and Applications (14 papers)
- Partner nations
- IndiaUnited KingdomUnited States
In The Last Decade
Ashwin Srinivasan
64 papers receiving 1.3k citations
Peers
Comparison fields: 5 of 129
- Artificial Intelligence 748
- Computational Theory and Mathematics 461
- Information Systems 368
- Molecular Biology 290
- Computer Vision and Pattern Recognition 155
Countries citing papers authored by Ashwin Srinivasan
This map shows the geographic impact of Ashwin Srinivasan'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 Ashwin Srinivasan with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ashwin Srinivasan more than expected).
Fields of papers citing papers by Ashwin Srinivasan
This network shows the impact of papers produced by Ashwin Srinivasan. 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 Ashwin Srinivasan. The network helps show where Ashwin Srinivasan may publish in the future.
Co-authorship network of co-authors of Ashwin Srinivasan
This figure shows the co-authorship network connecting the top 25 collaborators of Ashwin Srinivasan. A scholar is included among the top collaborators of Ashwin Srinivasan based on the total number of citations received by their joint publications. Widths of edges represent the number of papers authors have co-authored together. Node borders signify the number of papers an author published with Ashwin Srinivasan. Ashwin Srinivasan is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 0 | |
| 2 | 2 | |
| 3 | 4 | |
| 4 | 3 | |
| 5 | 2 | |
| 6 | 95 | |
| 7 | 6 | |
| 8 | 28 | |
| 9 | Neuro-Symbolic EDA-Based Optimization Using ILP-Enhanced DBNs. | 1 |
| 10 | 0 | |
| 11 | 6 | |
| 12 | 1 | |
| 13 | 10 | |
| 14 | Ilp: a short look back and a longer look forward | 19 |
| 15 | Query transformations for improving the efficiency of ilp systems | 24 |
| 16 | Discovering the Structure of Partial Differential Equations from Example Behaviour | 7 |
| 17 | Learning Chomsky-like Grammars for Biological Sequence Families | 3 |
| 18 | 23 | |
| 19 | 57 | |
| 20 | 18 |
About Ashwin Srinivasan
Ashwin Srinivasan is a scholar working on Computational Theory and Mathematics, Artificial Intelligence and Information Systems, having authored 76 papers that have together received 1.4k indexed citations. Recurring topics across this work include Logic, Reasoning, and Knowledge (20 papers), Computational Drug Discovery Methods (15 papers) and Data Mining Algorithms and Applications (14 papers). The work is most often cited by research in Computational Theory and Mathematics (461 citations), Artificial Intelligence (748 citations) and Information Systems (368 citations). Ashwin Srinivasan has collaborated with scholars based in India, United Kingdom and United States. Frequent co-authors include Ross D. King, Stephen Muggleton, Michael J.E. Sternberg, Stefan Krämer, Christoph Helma, David Page, Tirtharaj Dash, Hendrik Blockeel, David Page and Rui Camacho. Their work appears in journals such as Proceedings of the National Academy of Sciences, Bioinformatics and Scientific Reports.
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.