Dipankar Das
- Computer Vision and Pattern Recognition top 5%
- Artificial Intelligence top 5%
- Hardware and Architecture top 5%
- Computer Networks and Communications top 5%
- Electrical and Electronic Engineering
- Co-authors
- Bharat KaulHyoukjun KwonSudarshan SrinivasanAnanda SamajdarEric QinTushar KrishnaPradeep DubeySatya Gautam Vadlamudi
- Topics
- Retinal Imaging and Analysis (6 papers)Glaucoma and retinal disorders (5 papers)Digital Imaging for Blood Diseases (4 papers)
- Journals
- IEEE Transactions on Fuzzy SystemsIEEE Transactions on Instrumentation and MeasurementImage and Vision Computing
- Partner nations
- IndiaUnited StatesUnited Kingdom
In The Last Decade
Dipankar Das
14 papers receiving 598 citations
Hit Papers
Peers
Comparison fields: 5 of 70
- Computer Vision and Pattern Recognition 347
- Artificial Intelligence 221
- Hardware and Architecture 219
- Computer Networks and Communications 176
- Electrical and Electronic Engineering 171
Countries citing papers authored by Dipankar Das
This map shows the geographic impact of Dipankar Das'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 Dipankar Das with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Dipankar Das more than expected).
Fields of papers citing papers by Dipankar Das
This network shows the impact of papers produced by Dipankar Das. 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 Dipankar Das. The network helps show where Dipankar Das may publish in the future.
Co-authorship network of co-authors of Dipankar Das
This figure shows the co-authorship network connecting the top 25 collaborators of Dipankar Das. A scholar is included among the top collaborators of Dipankar Das 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 Dipankar Das. Dipankar Das 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 | 8 | |
| 3 | 4 | |
| 4 | 14 | |
| 5 | 32 | |
| 6 | 6 | |
| 7 | 3 | |
| 8 | SIGMA: A Sparse and Irregular GEMM Accelerator with Flexible Interconnects for DNN Trainingbreakdown → | 288 |
| 9 | 15 | |
| 10 | Mixed Precision Training of Convolutional Neural Networks using Integer Operations | 15 |
| 11 | 14 | |
| 12 | Parallel Efficient Sparse Matrix-Matrix Multiplication on Multicore Platforms. | 2 |
| 13 | 207 | |
| 14 | 3 | |
| 15 | 0 | |
| 16 | 6 |
About Dipankar Das
Dipankar Das is a scholar working on Computational Mathematics, Computer Vision and Pattern Recognition and Ophthalmology, having authored 16 papers that have together received 617 indexed citations. Recurring topics across this work include Retinal Imaging and Analysis (6 papers), Glaucoma and retinal disorders (5 papers) and Digital Imaging for Blood Diseases (4 papers). The work is most often cited by research in Computational Mathematics (20 citations), Hardware and Architecture (219 citations) and Computer Vision and Pattern Recognition (347 citations). Dipankar Das has collaborated with scholars based in India, United States and United Kingdom. Frequent co-authors include Bharat Kaul, Hyoukjun Kwon, Sudarshan Srinivasan, Ananda Samajdar, Eric Qin, Tushar Krishna, Pradeep Dubey, Satya Gautam Vadlamudi, Md. Mostofa Ali Patwary and Michael J. Anderson. Their work appears in journals such as IEEE Transactions on Fuzzy Systems, IEEE Transactions on Instrumentation and Measurement and Image and Vision Computing.
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.