Shubham Jain

1.7k total citations
33 papers, 721 citations indexed

About

Shubham Jain is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Mechanical Engineering. According to data from OpenAlex, Shubham Jain has authored 33 papers receiving a total of 721 indexed citations (citations by other indexed papers that have themselves been cited), including 19 papers in Electrical and Electronic Engineering, 8 papers in Artificial Intelligence and 7 papers in Mechanical Engineering. Recurrent topics in Shubham Jain's work include Advanced Memory and Neural Computing (16 papers), Ferroelectric and Negative Capacitance Devices (11 papers) and Advanced Neural Network Applications (6 papers). Shubham Jain is often cited by papers focused on Advanced Memory and Neural Computing (16 papers), Ferroelectric and Negative Capacitance Devices (11 papers) and Advanced Neural Network Applications (6 papers). Shubham Jain collaborates with scholars based in United States, India and Switzerland. Shubham Jain's co-authors include Anand Raghunathan, Kaushik Roy, Ashish Ranjan, Dibakar Rakshit, Kuldeep Kumar, Swagath Venkataramani, Leland Chang, Vijayalakshmi Srinivasan, Jungwook Choi and Aayush Ankit and has published in prestigious journals such as Proceedings of the IEEE, Applied Thermal Engineering and IEEE Transactions on Computers.

In The Last Decade

Shubham Jain

31 papers receiving 705 citations

Peers — A (Enhanced Table)

Peers by citation overlap · career bar shows stage (early→late) cites · hero ref

Name h Career Trend Papers Cites
Shubham Jain United States 15 528 134 95 93 82 33 721
Chenchen Deng China 15 374 0.7× 182 1.4× 58 0.6× 32 0.3× 160 2.0× 48 642
Avishek Biswas United States 7 603 1.1× 97 0.7× 87 0.9× 22 0.2× 85 1.0× 9 652
Wei Mao China 12 273 0.5× 36 0.3× 58 0.6× 55 0.6× 47 0.6× 56 476
Li Luo China 14 311 0.6× 97 0.7× 151 1.6× 16 0.2× 33 0.4× 88 599
Xi Jin China 11 525 1.0× 96 0.7× 66 0.7× 19 0.2× 43 0.5× 49 721
Supreet Jeloka United States 10 724 1.4× 92 0.7× 76 0.8× 40 0.4× 373 4.5× 23 866
Jongmin Lee South Korea 14 340 0.6× 40 0.3× 57 0.6× 34 0.4× 246 3.0× 78 704
Na Gong United States 12 376 0.7× 36 0.3× 57 0.6× 22 0.2× 72 0.9× 80 471
Marco Lanuzza Italy 23 1.4k 2.7× 79 0.6× 51 0.5× 13 0.1× 310 3.8× 133 1.7k
Qiming Zou China 12 218 0.4× 31 0.2× 23 0.2× 17 0.2× 46 0.6× 27 431

Countries citing papers authored by Shubham Jain

Since Specialization
Citations

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

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

20 of 20 papers shown
1.
Biswas, Sagnik, Shubham Jain, Poonam Coshic, et al.. (2025). Therapeutic Plasma Exchange in Patients With Acute Liver Failure: A Pilot Randomized Controlled Trial. Journal of Clinical and Experimental Hepatology. 16(1). 103178–103178.
2.
Simon, William, Irem Boybat, Gagandeep Singh, et al.. (2025). CiMBA: Accelerating Genome Sequencing Through On-Device Basecalling via Compute-in-Memory. IEEE Transactions on Parallel and Distributed Systems. 36(6). 1130–1145. 1 indexed citations
3.
Jain, Shubham, Kuldeep Kumar, Dibakar Rakshit, B. Premachandran, & K.S. Reddy. (2024). Single or multiple phase change materials? Understanding the melting and solidification behavior during real-time operations. Journal of Energy Storage. 107. 114940–114940. 1 indexed citations
4.
Jain, Shubham, Kuldeep Kumar, Dibakar Rakshit, B. Premachandran, & K.S. Reddy. (2023). Cyclic performance assessment of medium-temperature cascade thermal energy storage. Journal of Energy Storage. 68. 107662–107662. 9 indexed citations
5.
Jain, Shubham, Kuldeep Kumar, Dibakar Rakshit, B. Premachandran, & K.S. Reddy. (2023). Study on the melting dynamics of latent heat storage for various orientations, shell shapes, and eccentricity. Thermal Science and Engineering Progress. 45. 102087–102087. 15 indexed citations
6.
Jain, Shubham, Kuldeep Kumar, Dibakar Rakshit, B. Premachandran, & K.S. Reddy. (2023). Influence of the storage orientation and shell shape on the melting dynamics of shell and tube-type cascade latent heat storage. Applied Thermal Engineering. 231. 120923–120923. 18 indexed citations
7.
8.
Shukla, Shipra, et al.. (2023). AWARENESS AND ATTITUDE OF GERIATRIC PATIENTS REGARDING IMPLANT PROSTHODONTIC OPTIONS. 2(1). 22–27. 1 indexed citations
9.
Elangovan, R., Shubham Jain, & Anand Raghunathan. (2022). Ax-BxP: Approximate Blocked Computation for Precision-reconfigurable Deep Neural Network Acceleration. ACM Transactions on Design Automation of Electronic Systems. 27(3). 1–20. 3 indexed citations
10.
Jain, Shubham, Hsinyu Tsai, R. Muralidhar, et al.. (2022). A Heterogeneous and Programmable Compute-In-Memory Accelerator Architecture for Analog-AI Using Dense 2-D Mesh. IEEE Transactions on Very Large Scale Integration (VLSI) Systems. 31(1). 114–127. 28 indexed citations
11.
Jain, Shubham, K. Ravi Kumar, & Dibakar Rakshit. (2022). Thermal Performance Evaluation of Cascade Latent Heat Storage. 585–591. 1 indexed citations
12.
Jain, Shubham, et al.. (2021). A Framework for Adaptive Deep Reinforcement Semantic Parsing of Unstructured Data. 2021 International Conference on Information and Communication Technology Convergence (ICTC). 26. 1055–1060. 1 indexed citations
13.
Jain, Shubham, Arnab Raha, Vijay Raghunathan, et al.. (2020). Valley-Coupled-Spintronic Non-Volatile Memories With Compute-In-Memory Support. IEEE Transactions on Nanotechnology. 19. 635–647. 8 indexed citations
14.
Brooks, D., Martin M. Frank, Tayfun Gokmen, et al.. (2020). Emerging Neural Workloads and Their Impact on Hardware. HAL (Le Centre pour la Communication Scientifique Directe). 1462–1471. 3 indexed citations
15.
Jain, Shubham, Swagath Venkataramani, Vijayalakshmi Srinivasan, et al.. (2019). BiScaled-DNN. 1–6. 20 indexed citations
16.
Jain, Shubham, Abhronil Sengupta, Kaushik Roy, & Anand Raghunathan. (2018). Rx-Caffe: Framework for evaluating and training Deep Neural Networks on Resistive Crossbars.. arXiv (Cornell University). 7 indexed citations
17.
Jain, Shubham, Swagath Venkataramani, Vijayalakshmi Srinivasan, et al.. (2018). Compensated-DNN. 1–6. 33 indexed citations
18.
Jain, Shubham, Sachin S. Sapatnekar, Jianping Wang, Kaushik Roy, & Anand Raghunathan. (2018). Computing-in-memory with spintronics. 1640–1645. 14 indexed citations
19.
Jain, Shubham, Swagath Venkataramani, & Anand Raghunathan. (2016). Approximation through Logic Isolation for the Design of Quality Configurable Circuits. 612–617. 16 indexed citations
20.
Thomas, V. J., Seema Sharma, & Shubham Jain. (2009). Using Patents and Publications to Assess R&D Efficiency in the States of the USA. SSRN Electronic Journal. 11 indexed citations

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.

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