Krishna Teja Chitty-Venkata
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Electrical and Electronic Engineering
- Computer Networks and Communications
- Hardware and Architecture
- Topics
- Advanced Neural Network Applications (12 papers)Domain Adaptation and Few-Shot Learning (5 papers)Adversarial Robustness in Machine Learning (3 papers)
- Journals
- SHILAP Revista de lepidopterologíaIEEE AccessACM Computing Surveys
- Partner nations
- United StatesIndia
In The Last Decade
Krishna Teja Chitty-Venkata
14 papers receiving 225 citations
Peers
Comparison fields: 5 of 64
- Artificial Intelligence 93
- Computer Vision and Pattern Recognition 86
- Electrical and Electronic Engineering 59
- Computer Networks and Communications 23
- Hardware and Architecture 21
Countries citing papers authored by Krishna Teja Chitty-Venkata
This map shows the geographic impact of Krishna Teja Chitty-Venkata'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 Krishna Teja Chitty-Venkata with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Krishna Teja Chitty-Venkata more than expected).
Fields of papers citing papers by Krishna Teja Chitty-Venkata
This network shows the impact of papers produced by Krishna Teja Chitty-Venkata. 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 Krishna Teja Chitty-Venkata. The network helps show where Krishna Teja Chitty-Venkata may publish in the future.
Co-authorship network of co-authors of Krishna Teja Chitty-Venkata
This figure shows the co-authorship network connecting the top 25 collaborators of Krishna Teja Chitty-Venkata. A scholar is included among the top collaborators of Krishna Teja Chitty-Venkata 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 Krishna Teja Chitty-Venkata. Krishna Teja Chitty-Venkata 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 | 10 | |
| 3 | 7 | |
| 4 | 56 | |
| 5 | 2 | |
| 6 | 26 | |
| 7 | 48 | |
| 8 | 55 | |
| 9 | 5 | |
| 10 | 2 | |
| 11 | 1 | |
| 12 | 1 | |
| 13 | 5 | |
| 14 | 5 | |
| 15 | 6 |
About Krishna Teja Chitty-Venkata
Krishna Teja Chitty-Venkata is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Hardware and Architecture, having authored 15 papers that have together received 229 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (12 papers), Domain Adaptation and Few-Shot Learning (5 papers) and Adversarial Robustness in Machine Learning (3 papers). The work is most often cited by research in Computer Vision and Pattern Recognition (86 citations), Computational Mathematics (2 citations) and Hardware and Architecture (21 citations). Krishna Teja Chitty-Venkata has collaborated with scholars based in United States and India. Frequent co-authors include Arun K. Somani, Murali Emani, Venkatram Vishwanath, Sparsh Mittal and Valerie Taylor. Their work appears in journals such as SHILAP Revista de lepidopterología, IEEE Access and ACM Computing Surveys.
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.