Ashwin Srinivasan

3.2k total citations
76 papers, 1.4k citations indexed

About

Ashwin Srinivasan is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Molecular Biology. According to data from OpenAlex, Ashwin Srinivasan has authored 76 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Artificial Intelligence, 31 papers in Computational Theory and Mathematics and 16 papers in Molecular Biology. Recurrent topics in Ashwin Srinivasan's work include Logic, Reasoning, and Knowledge (20 papers), Computational Drug Discovery Methods (15 papers) and Data Mining Algorithms and Applications (14 papers). Ashwin Srinivasan is often cited by papers focused on Logic, Reasoning, and Knowledge (20 papers), Computational Drug Discovery Methods (15 papers) and Data Mining Algorithms and Applications (14 papers). Ashwin Srinivasan collaborates with scholars based in India, United Kingdom and United States. Ashwin Srinivasan's 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 and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Bioinformatics and Scientific Reports.

In The Last Decade

Ashwin Srinivasan

64 papers receiving 1.3k citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Ashwin Srinivasan India 20 748 461 368 290 155 76 1.4k
Jan Ramon Belgium 20 608 0.8× 212 0.5× 230 0.6× 312 1.1× 228 1.5× 101 1.4k
Siegfried Nijssen Belgium 20 683 0.9× 500 1.1× 777 2.1× 217 0.7× 258 1.7× 73 1.6k
Anton Schwaighofer Germany 18 577 0.8× 255 0.6× 229 0.6× 136 0.5× 246 1.6× 36 1.3k
Thomas Gärtner Germany 18 748 1.0× 306 0.7× 172 0.5× 294 1.0× 522 3.4× 88 1.5k
Tingyang Xu China 24 1.2k 1.7× 621 1.3× 422 1.1× 567 2.0× 436 2.8× 55 2.5k
Taneli Mielikäinen Finland 13 314 0.4× 202 0.4× 292 0.8× 210 0.7× 113 0.7× 21 832
Ricardo B. C. Prudêncio Brazil 19 711 1.0× 256 0.6× 262 0.7× 257 0.9× 113 0.7× 91 1.3k
Amedeo Napoli France 16 612 0.8× 523 1.1× 458 1.2× 296 1.0× 64 0.4× 148 1.2k
Shuhong Gao United States 22 618 0.8× 396 0.9× 404 1.1× 138 0.5× 162 1.0× 87 1.3k
Jianying Hu United States 18 695 0.9× 269 0.6× 340 0.9× 254 0.9× 512 3.3× 47 1.5k

Countries citing papers authored by Ashwin Srinivasan

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
1.
Srinivasan, Ashwin, et al.. (2025). A model for intelligible interaction between agents that predict and explain. Machine Learning. 114(4).
2.
Shekhar, Shashank, et al.. (2025). Exploring chemical space for “druglike” small molecules in the age of AI. Frontiers in Molecular Biosciences. 12. 1553667–1553667. 2 indexed citations
3.
Srinivasan, Ashwin, Tirtharaj Dash, Sowmya Ramaswamy Krishnan, et al.. (2024). Generating Novel Leads for Drug Discovery Using LLMs with Logical Feedback. Proceedings of the AAAI Conference on Artificial Intelligence. 38(1). 21–29. 4 indexed citations
4.
Srinivasan, Ashwin, et al.. (2024). Automated Pneumonia Classification Using DensePneumoNet in Chest CT Scans. 1907–1914. 3 indexed citations
5.
Srinivasan, Ashwin, et al.. (2023). Composition of relational features with an application to explaining black-box predictors. Machine Learning. 113(3). 1091–1132. 2 indexed citations
6.
Dash, Tirtharaj, et al.. (2022). A review of some techniques for inclusion of domain-knowledge into deep neural networks. Scientific Reports. 12(1). 1040–1040. 95 indexed citations
7.
Khadilkar, Harshad, et al.. (2019). A Reinforcement Learning Framework for Container Selection and Ship Load Sequencing in Ports. 2250–2252. 6 indexed citations
8.
Srinivasan, Ashwin. (2018). IoT Cloud Based Real Time Automobile Monitoring System. 231–235. 28 indexed citations
9.
Vig, Lovekesh, et al.. (2016). Neuro-Symbolic EDA-Based Optimization Using ILP-Enhanced DBNs.. Neural Information Processing Systems. 1 indexed citations
10.
Srinivasan, Ashwin, et al.. (2014). The Effect of Cooking Method on the Amount of Fat in an Egg. Journal of Emerging Investigators.
11.
Srinivasan, Ashwin & Ross D. King. (2008). Incremental Identification of Qualitative Models of Biological Systems using Inductive Logic Programming. Journal of Machine Learning Research. 9(48). 1475–1533. 6 indexed citations
12.
Camacho, Rui, Ross D. King, & Ashwin Srinivasan. (2004). Inductive logic programming : 14th International Conference, ILP 2004, Porto, Portugal, September 6-8, 2004 : proceedings. DIAL (Catholic University of Leuven). 1 indexed citations
13.
Srinivasan, Ashwin, Ross D. King, & Michael Bain. (2003). An empirical study of the use of relevance information in inductive logic programming. Journal of Machine Learning Research. 4. 369–383. 10 indexed citations
14.
Page, David & Ashwin Srinivasan. (2003). Ilp: a short look back and a longer look forward. Journal of Machine Learning Research. 4. 415–430. 19 indexed citations
15.
Costa, Vı́tor Santos, Ashwin Srinivasan, Rui Camacho, et al.. (2003). Query transformations for improving the efficiency of ilp systems. Journal of Machine Learning Research. 4(4). 465–491. 24 indexed citations
16.
Todorovski, Ljupčo, Sašo Džeroski, Ashwin Srinivasan, Jonathan Whiteley, & David J. Gavaghan. (2000). Discovering the Structure of Partial Differential Equations from Example Behaviour. International Conference on Machine Learning. 991–998. 7 indexed citations
17.
Muggleton, Stephen, Christopher H. Bryant, & Ashwin Srinivasan. (2000). Learning Chomsky-like Grammars for Biological Sequence Families. University of Salford Institutional Repository (University of Salford). 631–638. 3 indexed citations
18.
Srinivasan, Ashwin & Rui Camacho. (1999). Numerical reasoning with an ILP system capable of lazy evaluation and customised search. The Journal of Logic Programming. 40(2-3). 185–213. 23 indexed citations
19.
Srinivasan, Ashwin, Ross D. King, Stephen Muggleton, & Michael J.E. Sternberg. (1997). The predictive toxicology evaluation challenge. International Journal of Biological Macromolecules. 50(4). 4–9. 57 indexed citations
20.
King, Ross D. & Ashwin Srinivasan. (1997). The discovery of indicator variables for QSAR using inductive logic programming. Journal of Computer-Aided Molecular Design. 11(6). 571–580. 18 indexed citations

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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