Avishek Saha
- Artificial Intelligence top 5%
- Computer Vision and Pattern Recognition top 5%
- Management Science and Operations Research top 5%
- Signal Processing top 5%
- Computer Networks and Communications top 10%
- Co-authors
- Hal DauméAbhishek KumarSuresh VenkatasubramanianDivesh SrivastavaPiyush RaiShamik SuralJayanta MukherjeeLukasz Golab
- Topics
- Chemical and Physical Properties in Aqueous Solutions (11 papers)Crystallization and Solubility Studies (10 papers)Thermodynamic properties of mixtures (9 papers)
- Journals
- Chemical Physics LettersIEEE Transactions on Neural Networks and Learning SystemsIEEE Transactions on Knowledge and Data Engineering
- Partner nations
- IndiaUnited StatesRussia
In The Last Decade
Avishek Saha
33 papers receiving 689 citations
Peers
Comparison fields: 5 of 115
- Artificial Intelligence 412
- Computer Vision and Pattern Recognition 233
- Management Science and Operations Research 123
- Signal Processing 112
- Computer Networks and Communications 100
Countries citing papers authored by Avishek Saha
This map shows the geographic impact of Avishek Saha'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 Avishek Saha with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Avishek Saha more than expected).
Fields of papers citing papers by Avishek Saha
This network shows the impact of papers produced by Avishek Saha. 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 Avishek Saha. The network helps show where Avishek Saha may publish in the future.
Co-authorship network of co-authors of Avishek Saha
This figure shows the co-authorship network connecting the top 25 collaborators of Avishek Saha. A scholar is included among the top collaborators of Avishek Saha 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 Avishek Saha. Avishek Saha 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 | 0 | |
| 3 | 1 | |
| 4 | 4 | |
| 5 | 9 | |
| 6 | 9 | |
| 7 | 12 | |
| 8 | 3 | |
| 9 | 4 | |
| 10 | 2 | |
| 11 | 0 | |
| 12 | 1 | |
| 13 | 18 | |
| 14 | 17 | |
| 15 | 2 | |
| 16 | 7 | |
| 17 | Online Learning of Multiple Tasks and Their Relationships | 37 |
| 18 | 21 | |
| 19 | Co-regularization Based Semi-supervised Domain Adaptation | 99 |
| 20 | Domain Adaptation meets Active Learning | 69 |
About Avishek Saha
Avishek Saha is a scholar working on Filtration and Separation, Fluid Flow and Transfer Processes and Signal Processing, having authored 36 papers that have together received 717 indexed citations. Recurring topics across this work include Chemical and Physical Properties in Aqueous Solutions (11 papers), Crystallization and Solubility Studies (10 papers) and Thermodynamic properties of mixtures (9 papers). The work is most often cited by research in Filtration and Separation (40 citations), Artificial Intelligence (412 citations) and Computer Vision and Pattern Recognition (233 citations). Avishek Saha has collaborated with scholars based in India, United States and Russia. Frequent co-authors include Hal Daumé, Abhishek Kumar, Suresh Venkatasubramanian, Divesh Srivastava, Piyush Rai, Shamik Sural, Jayanta Mukherjee, Lukasz Golab, Flip Korn and Howard Karloff. Their work appears in journals such as Chemical Physics Letters, IEEE Transactions on Neural Networks and Learning Systems and IEEE Transactions on Knowledge and Data Engineering.
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.