Sumanth Dathathri
- Computational Theory and Mathematics
- Software
- Artificial Intelligence
- Hardware and Architecture
- Control and Systems Engineering
- Co-authors
- Richard M. MurrayScott C. LivingstonNecmiye ÖzayC. BalajiSicun GaoNikos AréchigaJonathan UesatoManish Kumar
- Topics
- Logic, programming, and type systems (3 papers)Adversarial Robustness in Machine Learning (3 papers)Formal Methods in Verification (3 papers)
- Journals
- International Communications in Heat and Mass TransferCaltechAUTHORS (California Institute of Technology)arXiv (Cornell University)
- Partner nations
- United StatesSwitzerlandIndia
In The Last Decade
Sumanth Dathathri
7 papers receiving 52 citations
Peers
Comparison fields: 5 of 27
- Computational Theory and Mathematics 30
- Software 19
- Artificial Intelligence 18
- Hardware and Architecture 8
- Control and Systems Engineering 7
Countries citing papers authored by Sumanth Dathathri
This map shows the geographic impact of Sumanth Dathathri'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 Sumanth Dathathri with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Sumanth Dathathri more than expected).
Fields of papers citing papers by Sumanth Dathathri
This network shows the impact of papers produced by Sumanth Dathathri. 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 Sumanth Dathathri. The network helps show where Sumanth Dathathri may publish in the future.
Co-authorship network of co-authors of Sumanth Dathathri
This figure shows the co-authorship network connecting the top 25 collaborators of Sumanth Dathathri. A scholar is included among the top collaborators of Sumanth Dathathri 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 Sumanth Dathathri. Sumanth Dathathri is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 2 | |
| 2 | 1 | |
| 3 | 0 | |
| 4 | Measuring the Robustness of Neural Networks via Minimal Adversarial Examples | 1 |
| 5 | 2 | |
| 6 | 1 | |
| 7 | 38 | |
| 8 | 8 |
About Sumanth Dathathri
Sumanth Dathathri is a scholar working on Software, Artificial Intelligence and Computational Theory and Mathematics, having authored 8 papers that have together received 53 indexed citations. Recurring topics across this work include Logic, programming, and type systems (3 papers), Adversarial Robustness in Machine Learning (3 papers) and Formal Methods in Verification (3 papers). The work is most often cited by research in Software (19 citations), Computational Theory and Mathematics (30 citations) and Hardware and Architecture (8 citations). Sumanth Dathathri has collaborated with scholars based in United States, Switzerland and India. Frequent co-authors include Richard M. Murray, Scott C. Livingston, Necmiye Özay, C. Balaji, Sicun Gao, Nikos Aréchiga, Jonathan Uesato, Manish Kumar, Robert Stanforth and Stephan Zheng. Their work appears in journals such as International Communications in Heat and Mass Transfer, CaltechAUTHORS (California Institute of Technology) and arXiv (Cornell University).
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.