Arnab Ganguly
- Molecular Biology
- Artificial Intelligence
- Computational Theory and Mathematics top 10%
- Finance top 10%
- Mathematical Physics
- Co-authors
- Heinz KoepplSharma V. ThankachanPaul DupuisAmarjit BudhirajaRahul ShahOsman TuranJ.E. BosC. LEWIS
- Topics
- Algorithms and Data Compression (19 papers)DNA and Biological Computing (13 papers)Network Packet Processing and Optimization (9 papers)
- Partner nations
- United StatesTaiwanSwitzerland
In The Last Decade
Arnab Ganguly
36 papers receiving 271 citations
Peers
Comparison fields: 5 of 94
- Molecular Biology 102
- Artificial Intelligence 67
- Computational Theory and Mathematics 60
- Finance 41
- Mathematical Physics 26
Countries citing papers authored by Arnab Ganguly
This map shows the geographic impact of Arnab Ganguly'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 Arnab Ganguly with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Arnab Ganguly more than expected).
Fields of papers citing papers by Arnab Ganguly
This network shows the impact of papers produced by Arnab Ganguly. 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 Arnab Ganguly. The network helps show where Arnab Ganguly may publish in the future.
Co-authorship network of co-authors of Arnab Ganguly
This figure shows the co-authorship network connecting the top 25 collaborators of Arnab Ganguly. A scholar is included among the top collaborators of Arnab Ganguly 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 Arnab Ganguly. Arnab Ganguly is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 6 | |
| 2 | 1 | |
| 3 | 4 | |
| 4 | 0 | |
| 5 | 8 | |
| 6 | 1 | |
| 7 | 3 | |
| 8 | 5 | |
| 9 | 5 | |
| 10 | A variational approach to path estimation and parameter inference of hidden diffusion processes | 2 |
| 11 | 1 | |
| 12 | 38 | |
| 13 | 7 | |
| 14 | 2 | |
| 15 | 6 | |
| 16 | 17 | |
| 17 | 21 | |
| 18 | Comparative evaluation of the IPCC AR5 CMIP5 versus the AR4 CMIP3 model ensembles for regional precipitation and their extremes over South America | 1 |
| 19 | 10 | |
| 20 | 40 |
About Arnab Ganguly
Arnab Ganguly is a scholar working on Hardware and Architecture, Artificial Intelligence and Computational Theory and Mathematics, having authored 40 papers that have together received 280 indexed citations. Recurring topics across this work include Algorithms and Data Compression (19 papers), DNA and Biological Computing (13 papers) and Network Packet Processing and Optimization (9 papers). The work is most often cited by research in Modeling and Simulation (21 citations), Finance (41 citations) and Computational Theory and Mathematics (60 citations). Arnab Ganguly has collaborated with scholars based in United States, Taiwan and Switzerland. Frequent co-authors include Heinz Koeppl, Sharma V. Thankachan, Paul Dupuis, Amarjit Budhiraja, Rahul Shah, Osman Turan, J.E. Bos, C. LEWIS, Michael Klann and Wing-Kai Hon. Their work appears in journals such as Bioinformatics, IEEE Access and Journal of Machine Learning Research.
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.