Abhronil Sengupta

3.3k total citations
79 papers, 2.2k citations indexed

About

Abhronil Sengupta is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Cognitive Neuroscience. According to data from OpenAlex, Abhronil Sengupta has authored 79 papers receiving a total of 2.2k indexed citations (citations by other indexed papers that have themselves been cited), including 71 papers in Electrical and Electronic Engineering, 28 papers in Artificial Intelligence and 17 papers in Cognitive Neuroscience. Recurrent topics in Abhronil Sengupta's work include Advanced Memory and Neural Computing (67 papers), Ferroelectric and Negative Capacitance Devices (46 papers) and Neural Networks and Reservoir Computing (16 papers). Abhronil Sengupta is often cited by papers focused on Advanced Memory and Neural Computing (67 papers), Ferroelectric and Negative Capacitance Devices (46 papers) and Neural Networks and Reservoir Computing (16 papers). Abhronil Sengupta collaborates with scholars based in United States, India and United Kingdom. Abhronil Sengupta's co-authors include Kaushik Roy, Aayush Ankit, Priyadarshini Panda, Gopalakrishnan Srinivasan, Yong Shim, Yusung Kim, Bing Han, Deliang Fan, Anand Raghunathan and Karthik Yogendra and has published in prestigious journals such as Science, Advanced Materials and SHILAP Revista de lepidopterología.

In The Last Decade

Abhronil Sengupta

77 papers receiving 2.1k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Abhronil Sengupta United States 27 1.8k 739 362 339 296 79 2.2k
Akhilesh Jaiswal United States 17 2.1k 1.2× 660 0.9× 503 1.4× 187 0.6× 422 1.4× 60 2.4k
Catherine E. Graves United States 18 3.0k 1.7× 605 0.8× 399 1.1× 166 0.5× 1.1k 3.7× 36 3.3k
Angeliki Pantazi Switzerland 21 1.6k 0.9× 324 0.4× 272 0.8× 545 1.6× 434 1.5× 85 2.3k
Seung Hwan Lee United States 17 2.4k 1.4× 983 1.3× 625 1.7× 82 0.2× 606 2.0× 36 2.6k
Tomáš Tůma Switzerland 14 2.2k 1.2× 517 0.7× 393 1.1× 281 0.8× 786 2.7× 27 2.6k
Boxun Li China 31 2.0k 1.1× 342 0.5× 105 0.3× 455 1.3× 352 1.2× 62 2.5k
Eric Montgomery United States 15 2.5k 1.4× 459 0.6× 432 1.2× 195 0.6× 952 3.2× 50 2.7k
Noraica Dávila United States 17 2.8k 1.6× 509 0.7× 426 1.2× 98 0.3× 1.1k 3.6× 23 2.9k
Deliang Fan United States 33 2.3k 1.3× 1.2k 1.6× 97 0.3× 463 1.4× 172 0.6× 179 3.2k

Countries citing papers authored by Abhronil Sengupta

Since Specialization
Citations

This map shows the geographic impact of Abhronil Sengupta'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 Abhronil Sengupta with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Abhronil Sengupta more than expected).

Fields of papers citing papers by Abhronil Sengupta

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Abhronil Sengupta. 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 Abhronil Sengupta. The network helps show where Abhronil Sengupta may publish in the future.

Co-authorship network of co-authors of Abhronil Sengupta

This figure shows the co-authorship network connecting the top 25 collaborators of Abhronil Sengupta. A scholar is included among the top collaborators of Abhronil Sengupta 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 Abhronil Sengupta. Abhronil Sengupta 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.
Sengupta, Abhronil, et al.. (2024). SpikingBERT: Distilling BERT to Train Spiking Language Models Using Implicit Differentiation. Proceedings of the AAAI Conference on Artificial Intelligence. 38(10). 10998–11006. 25 indexed citations
2.
Lu, Sen, et al.. (2024). Device Feasibility Analysis of Multi-level FeFETs for Neuromorphic Computing. 327–331. 1 indexed citations
3.
Ni, Kai, et al.. (2024). Variation-Resilient FeFET-Based In-Memory Computing Leveraging Probabilistic Deep Learning. IEEE Transactions on Electron Devices. 71(5). 2963–2969. 2 indexed citations
4.
Islam, A N M Nafiul, et al.. (2024). Hardware in Loop Learning with Spin Stochastic Neurons. SHILAP Revista de lepidopterología. 6(7). 3 indexed citations
5.
Islam, A N M Nafiul, et al.. (2023). Hybrid stochastic synapses enabled by scaled ferroelectric field-effect transistors. Applied Physics Letters. 122(12). 6 indexed citations
6.
Deng, Sunbin, Haoming Yu, Tae Joon Park, et al.. (2023). Selective area doping for Mott neuromorphic electronics. Science Advances. 9(11). eade4838–eade4838. 38 indexed citations
7.
Deng, Sunbin, Tae Joon Park, Haoming Yu, et al.. (2023). Hydrogenated VO2 Bits for Probabilistic Computing. IEEE Electron Device Letters. 44(10). 1776–1779. 6 indexed citations
8.
Sengupta, Abhronil, et al.. (2023). Sequence Learning Using Equilibrium Propagation. 2949–2957. 4 indexed citations
9.
Yu, Haoming, A N M Nafiul Islam, Sandip Mondal, Abhronil Sengupta, & Shriram Ramanathan. (2022). Switching Dynamics in Vanadium Dioxide-Based Stochastic Thermal Neurons. IEEE Transactions on Electron Devices. 69(6). 3135–3141. 9 indexed citations
10.
Zhang, Haitian, Tae Joon Park, A N M Nafiul Islam, et al.. (2022). Reconfigurable perovskite nickelate electronics for artificial intelligence. Science. 375(6580). 533–539. 166 indexed citations
11.
Rathi, Nitin, Indranil Chakraborty, Adarsh Kumar Kosta, et al.. (2022). Exploring Neuromorphic Computing Based on Spiking Neural Networks: Algorithms to Hardware. ACM Computing Surveys. 55(12). 1–49. 106 indexed citations
12.
Sengupta, Abhronil, et al.. (2019). Programmable Non-Volatile Memory Design Featuring Reconfigurable In-Memory Operations. 1–5. 6 indexed citations
13.
Chen, Mei‐Chin, Abhronil Sengupta, & Kaushik Roy. (2018). Magnetic Skyrmion as a Spintronic Deep Learning Spiking Neuron Processor. IEEE Transactions on Magnetics. 54(8). 1–7. 47 indexed citations
14.
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
15.
Ankit, Aayush, et al.. (2017). An All-Memristor Deep Spiking Neural Network: A Step Towards Realizing the Low Power, Stochastic Brain.. arXiv (Cornell University). 4 indexed citations
16.
Ankit, Aayush, Abhronil Sengupta, & Kaushik Roy. (2017). TraNNsformer: Neural Network Transformation for memristive crossbar based neuromorphic system design. arXiv (Cornell University). 533–540. 20 indexed citations
17.
Shim, Yong, Shuhan Chen, Abhronil Sengupta, & Kaushik Roy. (2017). Stochastic Spin-Orbit Torque Devices as Elements for Bayesian Inference. Scientific Reports. 7(1). 14101–14101. 31 indexed citations
18.
Srinivasan, Gopalakrishnan, Abhronil Sengupta, & Kaushik Roy. (2016). Magnetic Tunnel Junction Based Long-Term Short-Term Stochastic Synapse for a Spiking Neural Network with On-Chip STDP Learning. Scientific Reports. 6(1). 29545–29545. 159 indexed citations
19.
Sengupta, Abhronil, Yong Shim, & Kaushik Roy. (2015). Simulation studies of an All-Spin Artificial Neural Network: Emulating neural and synaptic functionalities through domain wall motion in ferromagnets.. arXiv (Cornell University). 2 indexed citations
20.
Bhattacharyya, Saugat, et al.. (2013). Automatic feature selection of motor imagery EEG signals using differential evolution and learning automata. Medical & Biological Engineering & Computing. 52(2). 131–139. 46 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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