Charbel Sakr

428 total citations
15 papers, 249 citations indexed

About

Charbel Sakr is a scholar working on Computer Vision and Pattern Recognition, Artificial Intelligence and Electrical and Electronic Engineering. According to data from OpenAlex, Charbel Sakr has authored 15 papers receiving a total of 249 indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Computer Vision and Pattern Recognition, 8 papers in Artificial Intelligence and 7 papers in Electrical and Electronic Engineering. Recurrent topics in Charbel Sakr's work include Advanced Neural Network Applications (8 papers), Advanced Memory and Neural Computing (5 papers) and Ferroelectric and Negative Capacitance Devices (5 papers). Charbel Sakr is often cited by papers focused on Advanced Neural Network Applications (8 papers), Advanced Memory and Neural Computing (5 papers) and Ferroelectric and Negative Capacitance Devices (5 papers). Charbel Sakr collaborates with scholars based in United States, France and Luxembourg. Charbel Sakr's co-authors include Naresh R. Shanbhag, Yongjune Kim, Sujan K. Gonugondla, Yingyan Lin, Siva Kumar Sastry Hari, Christopher W. Fletcher, Brian Zimmer, Sarita V. Adve, Pavlo Molchanov and Ben Keller and has published in prestigious journals such as IEEE Transactions on Signal Processing, IEEE Journal of Solid-State Circuits and Soft Matter.

In The Last Decade

Charbel Sakr

14 papers receiving 246 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Charbel Sakr United States	12	134	109	102	28	22	15	249
Burhan Ahmad Mudassar United States	9	139 1.0×	63 0.6×	99 1.0×	12 0.4×	41 1.9×	21	247
Jon J. Pimentel United States	6	158 1.2×	85 0.8×	136 1.3×	80 2.9×	86 3.9×	8	303
Mengshu Sun United States	8	161 1.2×	95 0.9×	135 1.3×	36 1.3×	20 0.9×	23	306
Maosen Li China	10	77 0.6×	113 1.0×	56 0.5×	7 0.3×	13 0.6×	16	249
Kailash Gopalakrishnan India	6	170 1.3×	128 1.2×	148 1.5×	45 1.6×	22 1.0×	7	300
Yun Long United States	10	329 2.5×	111 1.0×	108 1.1×	41 1.5×	29 1.3×	27	407
Zuying Luo China	11	190 1.4×	34 0.3×	48 0.5×	46 1.6×	24 1.1×	51	287
Philipp Gysel United States	4	197 1.5×	123 1.1×	251 2.5×	47 1.7×	25 1.1×	5	347
Chunshu Wu United States	8	111 0.8×	136 1.2×	121 1.2×	66 2.4×	42 1.9×	26	286
Chiraag Juvekar United States	11	117 0.9×	110 1.0×	111 1.1×	56 2.0×	57 2.6×	17	327

Countries citing papers authored by Charbel Sakr

Since Specialization

Citations

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

Fields of papers citing papers by Charbel Sakr

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Charbel Sakr

This figure shows the co-authorship network connecting the top 25 collaborators of Charbel Sakr. A scholar is included among the top collaborators of Charbel Sakr 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 Charbel Sakr. Charbel Sakr is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

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15 of 15 papers shown

Keller, Ben, Rangharajan Venkatesan, Steve Dai, et al.. (2023). A 95.6-TOPS/W Deep Learning Inference Accelerator With Per-Vector Scaled 4-bit Quantization in 5 nm. IEEE Journal of Solid-State Circuits. 58(4). 1129–1141. 30 indexed citations

Hari, Siva Kumar Sastry, Balakumar Sundaralingam, Thierry Tambe, et al.. (2023). VaPr: Variable-Precision Tensors to Accelerate Robot Motion Planning. 6304–6309. 2 indexed citations

Sakr, Charbel, Alina Vlad, Alessandro Coati, et al.. (2022). Unique orientation of 1D and 2D nanoparticle assemblies confined in smectic topological defects. Soft Matter. 18(25). 4792–4802. 11 indexed citations

Sakr, Charbel & Naresh R. Shanbhag. (2021). Signal Processing Methods to Enhance the Energy Efficiency of In-Memory Computing Architectures. IEEE Transactions on Signal Processing. 69. 6462–6472. 14 indexed citations

Mahmoud, Abdulrahman, Siva Kumar Sastry Hari, Christopher W. Fletcher, et al.. (2021). Optimizing Selective Protection for CNN Resilience. 127–138. 26 indexed citations

Gonugondla, Sujan K., et al.. (2020). KeyRAM: A 0.34 uJ/decision 18 k decisions/s Recurrent Attention In-memory Processor for Keyword Spotting. 1–4. 11 indexed citations

Gonugondla, Sujan K., et al.. (2020). A 0.44-μJ/dec, 39.9-μs/dec, Recurrent Attention In-Memory Processor for Keyword Spotting. IEEE Journal of Solid-State Circuits. 56(7). 2234–2244. 19 indexed citations

Gonugondla, Sujan K., et al.. (2020). Fundamental limits on the precision of in-memory architectures. 1–9. 15 indexed citations

Sakr, Charbel, Yongjune Kim, & Naresh R. Shanbhag. (2019). Minimum Precision Requirements of General Margin Hyperplane Classifiers. IEEE Journal on Emerging and Selected Topics in Circuits and Systems. 9(2). 253–266. 1 indexed citations

10.

Sakr, Charbel & Naresh R. Shanbhag. (2018). An Analytical Method to Determine Minimum Per-Layer Precision of Deep Neural Networks. 1090–1094. 22 indexed citations

11.

Sakr, Charbel & Naresh R. Shanbhag. (2018). Minimum Precision Requirements for Deep Learning with Biomedical Datasets. 18. 1–4. 1 indexed citations

12.

Sakr, Charbel, Jungwook Choi, Zhuo Wang, Kailash Gopalakrishnan, & Naresh R. Shanbhag. (2018). True Gradient-Based Training of Deep Binary Activated Neural Networks Via Continuous Binarization. 2346–2350. 12 indexed citations

13.

Sakr, Charbel, Yongjune Kim, & Naresh R. Shanbhag. (2017). Analytical guarantees on numerical precision of deep neural networks. Open Access System for Information Sharing (Pohang University of Science and Technology). 3007–3016. 38 indexed citations

14.

Lin, Yingyan, Charbel Sakr, Yongjune Kim, & Naresh R. Shanbhag. (2017). PredictiveNet: An energy-efficient convolutional neural network via zero prediction. 1–4. 35 indexed citations

15.

Sakr, Charbel, et al.. (2017). Minimum precision requirements for the SVM-SGD learning algorithm. 1138–1142. 12 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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