Urmish Thakker
- Artificial Intelligence top 5%
- Computer Networks and Communications top 5%
- Electrical and Electronic Engineering
- Information Systems top 10%
- Computer Science Applications top 5%
- Co-authors
- Shiqiang WangAhmed ImteajM. Hadi AminiJian LiGanesh DasikaMatthew MattinaIgor FedorovColby Banbury
- Topics
- Advanced Neural Network Applications (3 papers)Neural Networks and Applications (2 papers)Topic Modeling (2 papers)
- Partner nations
- United States
In The Last Decade
Urmish Thakker
8 papers receiving 496 citations
Hit Papers
Peers
Comparison fields: 5 of 53
- Artificial Intelligence 397
- Computer Networks and Communications 180
- Electrical and Electronic Engineering 99
- Information Systems 75
- Computer Science Applications 54
Countries citing papers authored by Urmish Thakker
This map shows the geographic impact of Urmish Thakker'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 Urmish Thakker with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Urmish Thakker more than expected).
Fields of papers citing papers by Urmish Thakker
This network shows the impact of papers produced by Urmish Thakker. 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 Urmish Thakker. The network helps show where Urmish Thakker may publish in the future.
Co-authorship network of co-authors of Urmish Thakker
This figure shows the co-authorship network connecting the top 25 collaborators of Urmish Thakker. A scholar is included among the top collaborators of Urmish Thakker 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 Urmish Thakker. Urmish Thakker is excluded from the visualization to improve readability, since they are connected to all nodes in the network.
All Works
| # | Work | Indexed citations |
|---|---|---|
| 1 | 3 | |
| 2 | Doping: A technique for Extreme Compression of LSTM Models using Sparse Structured Additive Matrices | 2 |
| 3 | A Survey on Federated Learning for Resource-Constrained IoT Devicesbreakdown → | 473 |
| 4 | 5 | |
| 5 | MicroNets: Neural Network Architectures for Deploying TinyML Applications on Commodity Microcontrollers | 6 |
| 6 | 5 | |
| 7 | 4 | |
| 8 | 13 |
About Urmish Thakker
Urmish Thakker is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Signal Processing, having authored 8 papers that have together received 511 indexed citations. Recurring topics across this work include Advanced Neural Network Applications (3 papers), Neural Networks and Applications (2 papers) and Topic Modeling (2 papers). The work is most often cited by research in Artificial Intelligence (397 citations), Computer Science Applications (54 citations) and Computer Networks and Communications (180 citations). Urmish Thakker has collaborated with scholars based in United States. Frequent co-authors include Shiqiang Wang, Ahmed Imteaj, M. Hadi Amini, Jian Li, Ganesh Dasika, Matthew Mattina, Igor Fedorov, Colby Banbury, Xueqin Huang and Vijay Janapa Reddi. Their work appears in journals such as IEEE Internet of Things Journal and ACM Journal on Emerging Technologies in Computing Systems.
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.