Ankit Jha
Impact in
- Oceanography top 5%
- Ocean Waves and Remote Sensing
- Oceanographic and Atmospheric Processes
- Environmental Engineering top 10%
- Hydrological Forecasting Using AI
Papers in
-
- Domain Adaptation and Few-Shot Learning 8
- Natural Language Processing Techniques 2
- Topic Modeling 2
-
- Multimodal Machine Learning Applications 6
- Advanced Neural Network Applications 4
- Advanced Image and Video Retrieval Techniques 3
- Co-authors
- M. C. Deo (1 shared paper)Biplab Banerjee (18 shared papers)Subhasis Chaudhuri (3 shared papers)Elisa Ricci (1 shared paper)Enrico Fini (1 shared paper)Shivam Pande (1 shared paper)A. Boccalatte (1 shared paper)Jocelyn Chanussot (1 shared paper)
In The Last Decade
Ankit Jha
19 papers receiving 398 citations
Peers
Comparison fields: 5 of 56
- Oceanography 183
- Environmental Engineering 150
- Earth-Surface Processes 52
- Media Technology 36
- Computer Vision and Pattern Recognition 82
Countries citing papers authored by Ankit Jha
This map shows the geographic impact of Ankit Jha'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 Ankit Jha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Ankit Jha more than expected).
Fields of papers citing papers by Ankit Jha
This network shows the impact of papers produced by Ankit Jha. 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 Ankit Jha. The network helps show where Ankit Jha may publish in the future.
Co-authors
The 16 scholars most cited alongside Ankit Jha, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
Showing the 20 most-cited of 24 papers — load more, or switch the sort, to bring in the rest.
| # | Work | ||
|---|---|---|---|
| 1 | 2001 | 260 | |
| 2 | 2023 | 23 | |
| 3 | 2023 | 23 | |
| 4 | 2023 | 20 | |
| 5 | 2024 | 15 | |
| 6 | 2020 | 14 | |
| 7 | 2023 | 8 | |
| 8 | 2024 | 7 | |
| 9 | 2025 | 7 | |
| 10 | 2023 | 6 | |
| 11 | 2019 | 5 | |
| 12 | 2023 | 5 | |
| 13 | 2024 | 5 | |
| 14 | 2021 | 3 | |
| 15 | 2024 | 2 | |
| 16 | 2024 | 2 | |
| 17 | 2021 | 2 | |
| 18 | 2016 | 1 | |
| 19 | 2023 | 1 | |
| 20 | 2025 | 0 |
About Ankit Jha
Ankit Jha is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Media Technology, Environmental Engineering and Computer Networks and Communications, having authored 24 papers that have together received 409 indexed citations. Recurring topics across this work include Domain Adaptation and Few-Shot Learning (8 papers), Multimodal Machine Learning Applications (6 papers), Advanced Neural Network Applications (4 papers), Remote-Sensing Image Classification (4 papers), Advanced Image and Video Retrieval Techniques (3 papers), Natural Language Processing Techniques (2 papers), Topic Modeling (2 papers) and Advanced Image Fusion Techniques (2 papers). The work is most often cited by research in Oceanography (183 citations), Environmental Engineering (150 citations), Earth-Surface Processes (52 citations), Media Technology (36 citations) and Computer Vision and Pattern Recognition (82 citations). Ankit Jha has collaborated with scholars based in India, Germany and Australia. Frequent co-authors include M. C. Deo, Biplab Banerjee, Subhasis Chaudhuri, Elisa Ricci, Enrico Fini, Shivam Pande, A. Boccalatte, Jocelyn Chanussot, Gemma Roig and Debabrata Pal. Their work appears in journals such as Briefings in Bioinformatics, Ocean Engineering, Computer Vision and Image Understanding, Solar Energy and IEEE Transactions on Geoscience and Remote Sensing.
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.