Dipesh Niraula

535 total citations
18 papers, 309 citations indexed

About

Dipesh Niraula is a scholar working on Electrical and Electronic Engineering, Artificial Intelligence and Radiology, Nuclear Medicine and Imaging. According to data from OpenAlex, Dipesh Niraula has authored 18 papers receiving a total of 309 indexed citations (citations by other indexed papers that have themselves been cited), including 11 papers in Electrical and Electronic Engineering, 6 papers in Artificial Intelligence and 6 papers in Radiology, Nuclear Medicine and Imaging. Recurrent topics in Dipesh Niraula's work include Advanced Memory and Neural Computing (7 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Ferroelectric and Negative Capacitance Devices (4 papers). Dipesh Niraula is often cited by papers focused on Advanced Memory and Neural Computing (7 papers), Radiomics and Machine Learning in Medical Imaging (6 papers) and Ferroelectric and Negative Capacitance Devices (4 papers). Dipesh Niraula collaborates with scholars based in United States, Canada and Netherlands. Dipesh Niraula's co-authors include В. Г. Карпов, Issam El Naqa, Randall K. Ten Haken, Yi Luo, I. V. Karpov, Sunan Cui, Lise Wei, M.M. Matuszak, Stephen R. Bowen and Issam M. El Naqa and has published in prestigious journals such as Applied Physics Letters, Journal of Applied Physics and Scientific Reports.

In The Last Decade

Dipesh Niraula

18 papers receiving 304 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Dipesh Niraula United States 11 132 67 65 43 29 18 309
Junyu Chen China 11 144 1.1× 161 2.4× 26 0.4× 12 0.3× 4 0.1× 35 488
Lionel Trojman Ecuador 11 296 2.2× 30 0.4× 39 0.6× 7 0.2× 2 0.1× 57 428
Xueyu Liu China 9 23 0.2× 49 0.7× 66 1.0× 17 0.4× 4 0.1× 48 254
M. Günhan Ertosun United States 9 107 0.8× 171 2.6× 173 2.7× 2 0.0× 16 0.6× 13 451
Haoyu Huang China 9 70 0.5× 16 0.2× 45 0.7× 6 0.1× 2 0.1× 40 305
Shashaanka Ashili United States 15 169 1.3× 18 0.3× 22 0.3× 11 0.3× 2 0.1× 32 504
William Lotter United States 9 24 0.2× 125 1.9× 132 2.0× 14 0.3× 81 2.8× 19 412
Y. Fridman United States 6 60 0.5× 51 0.8× 31 0.5× 17 0.4× 1 0.0× 10 562
Ling-Zhi Tang China 14 201 1.5× 10 0.1× 36 0.6× 58 1.3× 41 576
Yi Liang China 15 390 3.0× 12 0.2× 32 0.5× 6 0.1× 2 0.1× 45 677

Countries citing papers authored by Dipesh Niraula

Since Specialization
Citations

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

Fields of papers citing papers by Dipesh Niraula

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Dipesh Niraula

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

All Works

18 of 18 papers shown
1.
Niraula, Dipesh, Issam El Naqa, Jack A. Tuszyński, & Robert A. Gatenby. (2024). Modeling non-genetic information dynamics in cells using reservoir computing. iScience. 27(4). 109614–109614. 3 indexed citations
2.
Niraula, Dipesh, Jionghua Jin, Ivo D. Dinov, et al.. (2023). A clinical decision support system for AI-assisted decision-making in response-adaptive radiotherapy (ARCliDS). Scientific Reports. 13(1). 5279–5279. 27 indexed citations
3.
Cui, Sunan, Alberto Traverso, Dipesh Niraula, et al.. (2023). Interpretable artificial intelligence in radiology and radiation oncology. British Journal of Radiology. 96(1150). 20230142–20230142. 9 indexed citations
4.
Wei, Lise, Dipesh Niraula, Jie Fu, et al.. (2023). Artificial intelligence (AI) and machine learning (ML) in precision oncology: a review on enhancing discoverability through multiomics integration. British Journal of Radiology. 96(1150). 20230211–20230211. 63 indexed citations
5.
Niraula, Dipesh, Issam El Naqa, Randall K. Ten Haken, et al.. (2022). Precision radiotherapy via information integration of expert human knowledge and AI recommendation to optimize clinical decision making. Computer Methods and Programs in Biomedicine. 221. 106927–106927. 14 indexed citations
6.
Luo, Yi, Kyle C. Cuneo, Theodore S. Lawrence, et al.. (2022). A human-in-the-loop based Bayesian network approach to improve imbalanced radiation outcomes prediction for hepatocellular cancer patients with stereotactic body radiotherapy. Frontiers in Oncology. 12. 1061024–1061024. 5 indexed citations
7.
Niraula, Dipesh, et al.. (2022). A Decision Support Software for AI-Assisted Decision Making in Response-Adaptive Radiotherapy — An Evaluation Study. International Journal of Radiation Oncology*Biology*Physics. 114(3). e101–e102. 1 indexed citations
8.
Niraula, Dipesh, Sunan Cui, Lise Wei, et al.. (2022). Current status and future developments in predicting outcomes in radiation oncology. British Journal of Radiology. 95(1139). 20220239–20220239. 15 indexed citations
9.
Niraula, Dipesh, et al.. (2021). Quantum deep reinforcement learning for clinical decision support in oncology: application to adaptive radiotherapy. Scientific Reports. 11(1). 23545–23545. 34 indexed citations
10.
Карпов, В. Г. & Dipesh Niraula. (2019). OFF State Conduction in Filamentary RRAM. IEEE Electron Device Letters. 40(4). 550–553. 4 indexed citations
11.
Карпов, В. Г. & Dipesh Niraula. (2018). Resistive switching in nano-structures. Scientific Reports. 8(1). 12212–12212. 15 indexed citations
12.
Niraula, Dipesh & В. Г. Карпов. (2018). Comprehensive numerical modeling of filamentary RRAM devices including voltage ramp-rate and cycle-to-cycle variations. Journal of Applied Physics. 124(17). 33 indexed citations
13.
Карпов, В. Г., Dipesh Niraula, I. V. Karpov, & R. Kotlyar. (2017). Thermodynamics of Phase Transitions and Bipolar Filamentary Switching in Resistive Random-Access Memory. Physical Review Applied. 8(2). 24 indexed citations
14.
Subedi, Biwas, Dipesh Niraula, & В. Г. Карпов. (2017). The stochastic growth of metal whiskers. Applied Physics Letters. 110(25). 7 indexed citations
15.
Карпов, В. Г. & Dipesh Niraula. (2017). Log-Normal Statistics in Filamentary RRAM Devices and Related Systems. IEEE Electron Device Letters. 38(9). 1240–1243. 17 indexed citations
16.
Niraula, Dipesh, et al.. (2016). Electric field stimulated growth of Zn whiskers. AIP Advances. 6(7). 14 indexed citations
17.
Карпов, В. Г., Dipesh Niraula, & I. V. Karpov. (2016). Thermodynamic analysis of conductive filaments. Applied Physics Letters. 109(9). 14 indexed citations
18.
Niraula, Dipesh & В. Г. Карпов. (2015). The probabilistic distribution of metal whisker lengths. Journal of Applied Physics. 118(20). 10 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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