Rajeev Sahay

551 total citations
27 papers, 411 citations indexed

About

Rajeev Sahay is a scholar working on Artificial Intelligence, Water Science and Technology and Environmental Engineering. According to data from OpenAlex, Rajeev Sahay has authored 27 papers receiving a total of 411 indexed citations (citations by other indexed papers that have themselves been cited), including 12 papers in Artificial Intelligence, 8 papers in Water Science and Technology and 7 papers in Environmental Engineering. Recurrent topics in Rajeev Sahay's work include Hydrology and Watershed Management Studies (8 papers), Hydrological Forecasting Using AI (7 papers) and Adversarial Robustness in Machine Learning (6 papers). Rajeev Sahay is often cited by papers focused on Hydrology and Watershed Management Studies (8 papers), Hydrological Forecasting Using AI (7 papers) and Adversarial Robustness in Machine Learning (6 papers). Rajeev Sahay collaborates with scholars based in United States, India and South Africa. Rajeev Sahay's co-authors include Som Dutta, Vinit Sehgal, Christopher G. Brinton, Chandranath Chatterjee, David J. Love, Minjun Zhang, Joel B. Harley, Sungwon Kim, Seyyedali Hosseinalipour and Daniel O. Adams and has published in prestigious journals such as IEEE Access, IEEE/ACM Transactions on Networking and IEEE Sensors Journal.

In The Last Decade

Rajeev Sahay

26 papers receiving 401 citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Rajeev Sahay United States	10	244	203	99	98	60	27	411
Bahrudin Hrnjica Bosnia and Herzegovina	8	285 1.2×	228 1.1×	80 0.8×	167 1.7×	54 0.9×	15	462
Yusuf Essam Malaysia	7	184 0.8×	147 0.7×	94 0.9×	76 0.8×	72 1.2×	10	382
Anne Johannet France	12	268 1.1×	208 1.0×	48 0.5×	168 1.7×	23 0.4×	43	430
Kichul Jung South Korea	12	119 0.5×	209 1.0×	19 0.2×	175 1.8×	67 1.1×	39	452
Zhanya Xu China	10	206 0.8×	169 0.8×	56 0.6×	252 2.6×	120 2.0×	26	521
T.M.K.G. Fernando Australia	6	435 1.8×	320 1.6×	132 1.3×	236 2.4×	126 2.1×	9	677
Mohammad Zamani Iran	11	133 0.5×	128 0.6×	72 0.7×	64 0.7×	26 0.4×	26	365
Elham Rafiei Sardooi Iran	8	156 0.6×	186 0.9×	39 0.4×	205 2.1×	11 0.2×	10	471
Amin Mahdavi‐Meymand Iran	15	348 1.4×	232 1.1×	102 1.0×	170 1.7×	112 1.9×	40	656
Robert J. May Australia	6	335 1.4×	252 1.2×	88 0.9×	169 1.7×	63 1.1×	9	565

Countries citing papers authored by Rajeev Sahay

Since Specialization

Citations

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

Fields of papers citing papers by Rajeev Sahay

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rajeev Sahay

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

All Works

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20 of 20 papers shown

Khosravan, Naji, et al.. (2025). Bringing multi-modal multi-task federated foundation models to education domain: prospects and challenges. Frontiers in Artificial Intelligence. 8. 1683960–1683960.

Sahay, Rajeev, et al.. (2025). Mitigating Evasion Attacks in Federated Learning Based Signal Classifiers. IEEE Transactions on Network Science and Engineering. 12(5). 3933–3947. 1 indexed citations

Woodall, Christopher W., Javier G. P. Gamarra, Cang Hui, et al.. (2024). Forest types outpaced tree species in centroid-based range shifts under global change. Frontiers in Ecology and Evolution. 12. 2 indexed citations

Sahay, Rajeev, et al.. (2024). Revolutionizing AI-Assisted Education with Federated Learning: A Pathway to Distributed, Privacy-Preserving, and Debiased Learning Ecosystems. Proceedings of the AAAI Symposium Series. 3(1). 297–303. 3 indexed citations

Sahay, Rajeev, et al.. (2023). How Potent are Evasion Attacks for Poisoning Federated Learning-Based Signal Classifiers?. 2376–2381. 8 indexed citations

Sahay, Rajeev, Minjun Zhang, Tsung-Yen Yang, et al.. (2023). Predicting Learning Interactions in Social Learning Networks: A Deep Learning Enabled Approach. IEEE/ACM Transactions on Networking. 31(5). 2086–2100. 3 indexed citations

Sahay, Rajeev, et al.. (2022). An Uncertainty Quantification Framework for Counter Unmanned Aircraft Systems Using Deep Ensembles. IEEE Sensors Journal. 22(21). 20896–20909. 2 indexed citations

Sahay, Rajeev, S. Appadwedula, David J. Love, & Christopher G. Brinton. (2022). A Neural Network-Prepended GLRT Framework for Signal Detection Under Nonlinear Distortions. IEEE Communications Letters. 26(9). 2161–2165. 1 indexed citations

Sahay, Rajeev, et al.. (2022). Uncertainty Quantification-Based Unmanned Aircraft System Detection using Deep Ensembles. 2022 IEEE 95th Vehicular Technology Conference: (VTC2022-Spring). 1–5. 2 indexed citations

10.

Sahay, Rajeev & Christopher G. Brinton. (2021). Robust Subject-Independent P300 Waveform Classification via Signal Pre-Processing and Deep Learning. IEEE Access. 9. 87579–87591. 4 indexed citations

11.

Sahay, Rajeev, et al.. (2021). Hyperspectral Image Target Detection Using Deep Ensembles for Robust Uncertainty Quantification. 2021 55th Asilomar Conference on Signals, Systems, and Computers. 1715–1719. 1 indexed citations

12.

Kim, Sungwon, et al.. (2020). Efficient storage and processing of large guided wave data sets with random projections. Structural Health Monitoring. 20(5). 2513–2524. 9 indexed citations

13.

Sahay, Rajeev, et al.. (2018). Wavelet-genetic programming conjunction model for flood forecasting in rivers. Hydrology research. 49(6). 1880–1889. 10 indexed citations

14.

Sahay, Rajeev. (2015). Predicting Residence Time Of Pollutants In Transient Storage Zones Of Rivers By Genetic Programming. Zenodo (CERN European Organization for Nuclear Research). 9(2). 173–177. 2 indexed citations

15.

Sahay, Rajeev & Vinit Sehgal. (2014). Wavelet-ANFIS models for forecasting monsoon flows: Case study for the Gandak River (India). Water Resources. 41(5). 574–582. 10 indexed citations

16.

Sahay, Rajeev. (2013). Predicting Longitudinal Dispersion Coefficients in Sinuous Rivers by Genetic Algorithm. Journal of Hydrology and Hydromechanics. 61(3). 214–221. 26 indexed citations

17.

Sahay, Rajeev & Vinit Sehgal. (2012). Wavelet regression models for predicting flood stages in rivers: a case study in Eastern India. Journal of Flood Risk Management. 6(2). 146–155. 15 indexed citations

18.

Sahay, Rajeev. (2012). Predicting Transient Storage Model Parameters of Rivers by Genetic Algorithm. Water Resources Management. 26(13). 3667–3685. 8 indexed citations

19.

Sahay, Rajeev. (2010). Prediction of longitudinal dispersion coefficients in natural rivers using artificial neural network. Environmental Fluid Mechanics. 11(3). 247–261. 32 indexed citations

20.

Sahay, Rajeev & Som Dutta. (2009). Prediction of longitudinal dispersion coefficients in natural rivers using genetic algorithm. Hydrology research. 40(6). 544–552. 69 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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