Anindya Bijoy Das

813 total citations
43 papers, 583 citations indexed

About

Anindya Bijoy Das is a scholar working on Artificial Intelligence, Signal Processing and Cognitive Neuroscience. According to data from OpenAlex, Anindya Bijoy Das has authored 43 papers receiving a total of 583 indexed citations (citations by other indexed papers that have themselves been cited), including 21 papers in Artificial Intelligence, 13 papers in Signal Processing and 12 papers in Cognitive Neuroscience. Recurrent topics in Anindya Bijoy Das's work include Blind Source Separation Techniques (12 papers), EEG and Brain-Computer Interfaces (12 papers) and Stochastic Gradient Optimization Techniques (12 papers). Anindya Bijoy Das is often cited by papers focused on Blind Source Separation Techniques (12 papers), EEG and Brain-Computer Interfaces (12 papers) and Stochastic Gradient Optimization Techniques (12 papers). Anindya Bijoy Das collaborates with scholars based in United States, Bangladesh and Canada. Anindya Bijoy Das's co-authors include Mohammed Imamul Hassan Bhuiyan, Aditya Ramamoorthy, Samiul Alam, Li Tang, Christopher G. Brinton, David J. Love, Madan K. Bhattacharyya, Xiaoqiu Huang, Ifat Jahangir and Silvia R. Cianzio and has published in prestigious journals such as PLoS ONE, Scientific Reports and IEEE Transactions on Information Theory.

In The Last Decade

Anindya Bijoy Das

40 papers receiving 560 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Anindya Bijoy Das United States 12 331 244 180 121 60 43 583
Tomasz M. Rutkowski Japan 18 639 1.9× 358 1.5× 68 0.4× 70 0.6× 22 0.4× 88 955
Mitul Kumar Ahirwal India 15 311 0.9× 204 0.8× 129 0.7× 148 1.2× 42 0.7× 65 678
Mohammad-Parsa Hosseini United States 11 457 1.4× 146 0.6× 110 0.6× 108 0.9× 53 0.9× 14 766
Clive Cheong Took United Kingdom 11 408 1.2× 274 1.1× 106 0.6× 67 0.6× 10 0.2× 37 697
Clodoaldo A. M. Lima Brazil 13 254 0.8× 194 0.8× 215 1.2× 46 0.4× 16 0.3× 51 653
Manpreet Kaur India 13 112 0.3× 66 0.3× 175 1.0× 109 0.9× 22 0.4× 48 653
John V. Guttag United States 4 296 0.9× 184 0.8× 62 0.3× 81 0.7× 19 0.3× 6 386
Hong He China 12 123 0.4× 90 0.4× 167 0.9× 80 0.7× 14 0.2× 35 552
P. S. Sathidevi India 12 142 0.4× 215 0.9× 203 1.1× 14 0.1× 34 0.6× 71 594

Countries citing papers authored by Anindya Bijoy Das

Since Specialization
Citations

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

Fields of papers citing papers by Anindya Bijoy Das

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by Anindya Bijoy Das. 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 Anindya Bijoy Das. The network helps show where Anindya Bijoy Das may publish in the future.

Co-authorship network of co-authors of Anindya Bijoy Das

This figure shows the co-authorship network connecting the top 25 collaborators of Anindya Bijoy Das. A scholar is included among the top collaborators of Anindya Bijoy Das 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 Anindya Bijoy Das. Anindya Bijoy Das 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.
Das, Anindya Bijoy, et al.. (2025). Multi-Agent Reinforcement Learning for Graph Discovery in D2D-Enabled Federated Learning. IEEE Journal of Selected Topics in Signal Processing. 19(7). 1525–1541. 1 indexed citations
2.
Das, Anindya Bijoy, et al.. (2025). Cooperative Decentralized Backdoor Attacks on Vertical Federated Learning. 34. 2004–2019. 1 indexed citations
3.
Das, Anindya Bijoy, Aditya Ramamoorthy, David J. Love, & Christopher G. Brinton. (2024). Sparsity-Preserving Encodings for Straggler-Optimal Distributed Matrix Computations at the Edge. IEEE Internet of Things Journal. 11(21). 34455–34470. 1 indexed citations
4.
Lee, Byung-Hyun, Anindya Bijoy Das, David J. Love, Christopher G. Brinton, & James V. Krogmeier. (2024). Constant Modulus Waveform Design with Block-Level Interference Exploitation for DFRC Systems. 324–329. 2 indexed citations
5.
Das, Anindya Bijoy, et al.. (2024). Do Small Cells Make Sense for Simple Low Cost LPWANs?. IEEE Wireless Communications Letters. 13(9). 2352–2356.
6.
Lee, Seohyun, et al.. (2024). Smart Information Exchange for Unsupervised Federated Learning via Reinforcement Learning. 3494–3499. 1 indexed citations
7.
Das, Anindya Bijoy, Aditya Ramamoorthy, David J. Love, & Christopher G. Brinton. (2023). Distributed Matrix Computations with Low-weight Encodings. 1979–1984. 3 indexed citations
8.
Das, Anindya Bijoy, et al.. (2023). A Decentralized Pilot Assignment Algorithm for Scalable O-RAN Cell-Free Massive MIMO. IEEE Journal on Selected Areas in Communications. 42(2). 373–388. 11 indexed citations
9.
Das, Anindya Bijoy, Aditya Ramamoorthy, David J. Love, & Christopher G. Brinton. (2023). Distributed Matrix Computations With Low-Weight Encodings. IEEE Journal on Selected Areas in Information Theory. 4. 363–378. 4 indexed citations
10.
Sahoo, Dipak Kumar, Anindya Bijoy Das, Xiaoqiu Huang, Silvia R. Cianzio, & Madan K. Bhattacharyya. (2021). Tightly linked Rps12 and Rps13 genes provide broad-spectrum Phytophthora resistance in soybean. Scientific Reports. 11(1). 16907–16907. 17 indexed citations
11.
Das, Anindya Bijoy & Aditya Ramamoorthy. (2021). Coded sparse matrix computation schemes that leverage partial stragglers. 1570–1575. 13 indexed citations
12.
Swaminathan, Sivakumar, Anindya Bijoy Das, Teshale Assefa, et al.. (2019). Genome wide association study identifies novel single nucleotide polymorphic loci and candidate genes involved in soybean sudden death syndrome resistance. PLoS ONE. 14(2). e0212071–e0212071. 10 indexed citations
13.
Huang, Xiaoqiu, Anindya Bijoy Das, Binod Bihari Sahu, et al.. (2016). Identification of Highly Variable Supernumerary Chromosome Segments in an Asexual Pathogen. PLoS ONE. 11(6). e0158183–e0158183. 6 indexed citations
14.
Das, Anindya Bijoy & Mohammed Imamul Hassan Bhuiyan. (2016). Discrimination and classification of focal and non-focal EEG signals using entropy-based features in the EMD-DWT domain. Biomedical Signal Processing and Control. 29. 11–21. 178 indexed citations
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Bhuiyan, Mohammed Imamul Hassan & Anindya Bijoy Das. (2014). A subband correlation-based method for the automatic detection of epilepsy and seizure in the dual tree complex wavelet transform domain. 2. 811–816. 5 indexed citations
18.
Das, Anindya Bijoy, et al.. (2014). Seizure detection using Bessel k-form parameters in the empirical mode decomposition domain. 18. 1–5. 1 indexed citations
19.
Das, Anindya Bijoy, Mohammed Imamul Hassan Bhuiyan, & Samiul Alam. (2014). Statistical parameters in the dual tree complex wavelet transform domain for the detection of epilepsy and seizure. 64. 1–6. 13 indexed citations
20.
Sidhu, T.S., et al.. (2004). A client-server paradigm for protection studies. 303–308.

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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