Christos-Savvas Bouganis

3.0k total citations
124 papers, 1.8k citations indexed

About

Christos-Savvas Bouganis is a scholar working on Computer Vision and Pattern Recognition, Electrical and Electronic Engineering and Artificial Intelligence. According to data from OpenAlex, Christos-Savvas Bouganis has authored 124 papers receiving a total of 1.8k indexed citations (citations by other indexed papers that have themselves been cited), including 66 papers in Computer Vision and Pattern Recognition, 42 papers in Electrical and Electronic Engineering and 35 papers in Artificial Intelligence. Recurrent topics in Christos-Savvas Bouganis's work include Advanced Neural Network Applications (26 papers), CCD and CMOS Imaging Sensors (20 papers) and Embedded Systems Design Techniques (17 papers). Christos-Savvas Bouganis is often cited by papers focused on Advanced Neural Network Applications (26 papers), CCD and CMOS Imaging Sensors (20 papers) and Embedded Systems Design Techniques (17 papers). Christos-Savvas Bouganis collaborates with scholars based in United Kingdom, Greece and Cyprus. Christos-Savvas Bouganis's co-authors include Stylianos I. Venieris, Alexandros Kouris, Peter Y. K. Cheung, George A. Constantinides, Nur Ahmadi, Timothy G. Constandinou, Theocharis Theocharides, Christos Kyrkou, Dimitrios Tzovaras and Shuanglong Liu and has published in prestigious journals such as PLoS ONE, IEEE Transactions on Pattern Analysis and Machine Intelligence and Scientific Reports.

In The Last Decade

Christos-Savvas Bouganis

120 papers receiving 1.8k citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Christos-Savvas Bouganis United Kingdom 21 830 686 514 321 194 124 1.8k
Francesco Conti Italy 24 595 0.7× 999 1.5× 405 0.8× 513 1.6× 191 1.0× 102 2.0k
Mikel Luján United Kingdom 22 450 0.5× 552 0.8× 666 1.3× 555 1.7× 132 0.7× 148 2.2k
Marian Verhelst Belgium 33 775 0.9× 2.4k 3.5× 643 1.3× 421 1.3× 149 0.8× 225 3.6k
Javier Díaz Spain 21 777 0.9× 407 0.6× 138 0.3× 160 0.5× 221 1.1× 101 1.7k
Xingyu Liu China 7 1.3k 1.6× 642 0.9× 672 1.3× 234 0.7× 154 0.8× 15 1.9k
Majid Ahmadi Canada 22 293 0.4× 1.3k 1.8× 628 1.2× 154 0.5× 95 0.5× 282 2.2k
Huizi Mao United States 13 1.9k 2.3× 991 1.4× 1.2k 2.4× 372 1.2× 260 1.3× 15 2.9k
Xinming Huang United States 28 895 1.1× 751 1.1× 556 1.1× 115 0.4× 346 1.8× 177 2.6k
Ulrich Rückert Germany 22 213 0.3× 1.0k 1.5× 638 1.2× 524 1.6× 150 0.8× 255 2.2k
Caiwen Ding United States 22 617 0.7× 782 1.1× 833 1.6× 219 0.7× 50 0.3× 117 1.8k

Countries citing papers authored by Christos-Savvas Bouganis

Since Specialization
Citations

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

Fields of papers citing papers by Christos-Savvas Bouganis

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Christos-Savvas Bouganis. 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 Christos-Savvas Bouganis. The network helps show where Christos-Savvas Bouganis may publish in the future.

Co-authorship network of co-authors of Christos-Savvas Bouganis

This figure shows the co-authorship network connecting the top 25 collaborators of Christos-Savvas Bouganis. A scholar is included among the top collaborators of Christos-Savvas Bouganis 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 Christos-Savvas Bouganis. Christos-Savvas Bouganis 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.
Bouganis, Christos-Savvas, et al.. (2024). Auto WS: Automate Weights Streaming in Layer-Wise Pipelined DNN Accelerators. 1–6. 2 indexed citations
2.
Bouganis, Christos-Savvas, et al.. (2024). Augmenting the Softmax with Additional Confidence Scores for Improved Selective Classification with Out-of-Distribution Data. International Journal of Computer Vision. 132(9). 3714–3752. 1 indexed citations
3.
Bouganis, Christos-Savvas, et al.. (2024). Latency Driven Spatially Sparse Optimization for Multi-Branch CNNs for Semantic Segmentation. 949–957. 2 indexed citations
4.
5.
Bouganis, Christos-Savvas, et al.. (2023). SVD-NAS: Coupling Low-Rank Approximation and Neural Architecture Search. 2023 IEEE/CVF Winter Conference on Applications of Computer Vision (WACV). 1503–1512. 6 indexed citations
6.
Bouganis, Christos-Savvas, et al.. (2022). MIDAS: Mutual Information Driven Approximate Synthesis. Spiral (Imperial College London). 50–55. 1 indexed citations
7.
Trancoso, Pedro, et al.. (2020). Mapping Multiple LSTM models on FPGAs. Chalmers Research (Chalmers University of Technology). 1–9. 6 indexed citations
8.
Bouganis, Christos-Savvas, et al.. (2019). Real-Time Servo Press Force Estimation Based on Dual Particle Filter. IEEE Transactions on Industrial Electronics. 67(5). 4088–4097. 8 indexed citations
9.
Shafique, Muhammad, Theocharis Theocharides, Christos-Savvas Bouganis, et al.. (2018). An overview of next-generation architectures for machine learning: Roadmap, opportunities and challenges in the IoT era. Spiral (Imperial College London). 827–832. 63 indexed citations
10.
Kouris, Alexandros & Christos-Savvas Bouganis. (2018). Learning to Fly by MySelf: A Self-Supervised CNN-Based Approach for Autonomous Navigation. Spiral (Imperial College London). 1–9. 65 indexed citations
11.
Venieris, Stylianos I. & Christos-Savvas Bouganis. (2016). fpgaConvNet: A Framework for Mapping Convolutional Neural Networks on FPGAs. Spiral (Imperial College London). 40–47. 191 indexed citations
12.
Bottolo, Leonardo, et al.. (2016). Particle MCMC algorithms and architectures for accelerating inference in state-space models. International Journal of Approximate Reasoning. 83. 413–433. 15 indexed citations
13.
Bouganis, Christos-Savvas, et al.. (2016). Semi-dense SLAM on an FPGA SoC. Spiral (Imperial College London). 1–4. 39 indexed citations
14.
Bouganis, Christos-Savvas, et al.. (2015). ARC 2014. ACM Transactions on Reconfigurable Technology and Systems. 9(1). 1–15. 6 indexed citations
15.
Venieris, Stylianos I., et al.. (2015). Towards heterogeneous solvers for large-scale linear systems. 1–8. 3 indexed citations
16.
Duarte, Rui Policarpo & Christos-Savvas Bouganis. (2012). High-level linear projection circuit design optimization framework for FPGAs under over-clocking. 723–726. 3 indexed citations
17.
Bouganis, Christos-Savvas, et al.. (2010). A Heterogeneous FPGA Architecture for Support Vector Machine Training. 211–214. 24 indexed citations
18.
Bouganis, Christos-Savvas, et al.. (2010). Mapping Multiple Multivariate Gaussian Random Number Generators on an FPGA. 89–94. 2 indexed citations
19.
Bouganis, Christos-Savvas, et al.. (2008). A scalable FPGA architecture for non-linear SVM training. 337–340. 26 indexed citations
20.
Bouganis, Christos-Savvas, et al.. (2005). FPGA-Accelerated Reconstruction of Gene Regulatory Networks.. 323–328. 1 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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