Deepa Modi
Impact in
-
- Chalcogenide Semiconductor Thin Films
- solar cell performance optimization
- Advanced Semiconductor Detectors and Materials
- Perovskite Materials and Applications
-
- Geotechnical Engineering and Analysis
Papers in
-
- Natural Language Processing Techniques 3
- Algorithms and Data Compression 2
-
- Multimodal Machine Learning Applications 1
- Co-authors
- Deepankar Choudhury (1 shared paper)Gang Xiong (1 shared paper)Nathan R. Wolf (1 shared paper)Wyatt K. Metzger (1 shared paper)Rouin Farshchi (1 shared paper)Sachit Grover (1 shared paper)Xi Shan (1 shared paper)Neeta Nain (2 shared papers)
- Journals
- IEEE Journal of Photovoltaics (1 paper)Nature Environment and Pollution Technology (1 paper)Journal of Multimedia Information System (1 paper)
- Partner nations
- IndiaUnited States
In The Last Decade
Deepa Modi
6 papers receiving 78 citations
Peers
Comparison fields: 5 of 20
- Electrical and Electronic Engineering 60
- Safety, Risk, Reliability and Quality 9
- Materials Chemistry 38
- Management, Monitoring, Policy and Law 6
- Civil and Structural Engineering 10
Countries citing papers authored by Deepa Modi
This map shows the geographic impact of Deepa Modi'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 Deepa Modi with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Deepa Modi more than expected).
Fields of papers citing papers by Deepa Modi
This network shows the impact of papers produced by Deepa Modi. 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 Deepa Modi. The network helps show where Deepa Modi may publish in the future.
Co-authors
The 10 scholars most cited alongside Deepa Modi, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.
All Works
| # | Work | ||
|---|---|---|---|
| 1 | 2023 | 60 | |
| 2 | 2008 | 10 | |
| 3 | 2024 | 3 | |
| 4 | 2018 | 2 | |
| 5 | 2018 | 2 | |
| 6 | 2017 | 1 |
About Deepa Modi
Deepa Modi is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Civil and Structural Engineering, Pollution and Industrial and Manufacturing Engineering, having authored 6 papers that have together received 78 indexed citations. Recurring topics across this work include Natural Language Processing Techniques (3 papers), Algorithms and Data Compression (2 papers), Multimodal Machine Learning Applications (1 paper), Recycling and Waste Management Techniques (1 paper), Chalcogenide Semiconductor Thin Films (1 paper), Microplastics and Plastic Pollution (1 paper), Geotechnical Engineering and Underground Structures (1 paper) and Perovskite Materials and Applications (1 paper). The work is most often cited by research in Electrical and Electronic Engineering (60 citations), Safety, Risk, Reliability and Quality (9 citations), Materials Chemistry (38 citations), Management, Monitoring, Policy and Law (6 citations) and Civil and Structural Engineering (10 citations). Deepa Modi has collaborated with scholars based in India and United States. Frequent co-authors include Deepankar Choudhury, Gang Xiong, Nathan R. Wolf, Wyatt K. Metzger, Rouin Farshchi, Sachit Grover, Xi Shan, Neeta Nain, Prakash Choudhary and Mushtaq Ahmed. Their work appears in journals such as IEEE Journal of Photovoltaics, Nature Environment and Pollution Technology and Journal of Multimedia Information System.
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.