Shubham Jain
- Electrical and Electronic Engineering top 10%
- Artificial Intelligence top 10%
- Computer Vision and Pattern Recognition top 10%
- Mechanical Engineering
- Hardware and Architecture top 5%
- Co-authors
- Anand RaghunathanKaushik RoyAshish RanjanKuldeep KumarDibakar RakshitSwagath VenkataramaniLeland ChangVijayalakshmi Srinivasan
- Topics
- Advanced Memory and Neural Computing (16 papers)Ferroelectric and Negative Capacitance Devices (11 papers)Advanced Neural Network Applications (6 papers)
- Cited by
- Hardware and ArchitectureElectrical and Electronic EngineeringComputer Vision and Pattern Recognition
- Partner nations
- United StatesIndiaSwitzerland
In The Last Decade
Shubham Jain
31 papers receiving 705 citations
Peers
Comparison fields: 5 of 61
- Electrical and Electronic Engineering 528
- Artificial Intelligence 134
- Computer Vision and Pattern Recognition 95
- Mechanical Engineering 93
- Hardware and Architecture 82
Countries citing papers authored by Shubham Jain
This map shows the geographic impact of Shubham Jain'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 Shubham Jain with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Shubham Jain more than expected).
Fields of papers citing papers by Shubham Jain
This network shows the impact of papers produced by Shubham Jain. 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 Shubham Jain. The network helps show where Shubham Jain may publish in the future.
Co-authorship network of co-authors of Shubham Jain
This figure shows the co-authorship network connecting the top 25 collaborators of Shubham Jain. A scholar is included among the top collaborators of Shubham Jain 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 Shubham Jain. Shubham Jain 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 | 1 | |
| 3 | 1 | |
| 4 | 9 | |
| 5 | 15 | |
| 6 | 18 | |
| 7 | 2 | |
| 8 | 1 | |
| 9 | 28 | |
| 10 | 3 | |
| 11 | 1 | |
| 12 | 1 | |
| 13 | 8 | |
| 14 | 3 | |
| 15 | 20 | |
| 16 | 35 | |
| 17 | Rx-Caffe: Framework for evaluating and training Deep Neural Networks on Resistive Crossbars. | 7 |
| 18 | 33 | |
| 19 | 16 | |
| 20 | Using Patents and Publications to Assess R&D Efficiency in the States of the USA | 11 |
About Shubham Jain
Shubham Jain is a scholar working on General Dentistry, Electrical and Electronic Engineering and Computer Vision and Pattern Recognition, having authored 33 papers that have together received 721 indexed citations. Recurring topics across this work include Advanced Memory and Neural Computing (16 papers), Ferroelectric and Negative Capacitance Devices (11 papers) and Advanced Neural Network Applications (6 papers). The work is most often cited by research in Hardware and Architecture (82 citations), Electrical and Electronic Engineering (528 citations) and Computer Vision and Pattern Recognition (95 citations). Shubham Jain has collaborated with scholars based in United States, India and Switzerland. Frequent co-authors include Anand Raghunathan, Kaushik Roy, Ashish Ranjan, Kuldeep Kumar, Dibakar Rakshit, Swagath Venkataramani, Leland Chang, Vijayalakshmi Srinivasan, Jungwook Choi and K.S. Reddy. Their work appears in journals such as Proceedings of the IEEE, Applied Thermal Engineering and IEEE Transactions on Computers.
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.