J.S. Yuan

3.7k total citations
240 papers, 2.6k citations indexed

About

J.S. Yuan is a scholar working on Electrical and Electronic Engineering, Atomic and Molecular Physics, and Optics and Condensed Matter Physics. According to data from OpenAlex, J.S. Yuan has authored 240 papers receiving a total of 2.6k indexed citations (citations by other indexed papers that have themselves been cited), including 213 papers in Electrical and Electronic Engineering, 27 papers in Atomic and Molecular Physics, and Optics and 24 papers in Condensed Matter Physics. Recurrent topics in J.S. Yuan's work include Semiconductor materials and devices (121 papers), Advancements in Semiconductor Devices and Circuit Design (114 papers) and Radio Frequency Integrated Circuit Design (54 papers). J.S. Yuan is often cited by papers focused on Semiconductor materials and devices (121 papers), Advancements in Semiconductor Devices and Circuit Design (114 papers) and Radio Frequency Integrated Circuit Design (54 papers). J.S. Yuan collaborates with scholars based in United States, Taiwan and Iraq. J.S. Yuan's co-authors include Milad Salem, Jie Lin, Aminollah Khormali, Hong Yang, Arash Keshavarzi Arshadi, Juin J. Liou, Yier Jin, Ronald F. DeMara, Chulin Yu and Yu Bi and has published in prestigious journals such as SHILAP Revista de lepidopterología, Applied Physics Letters and Journal of Applied Physics.

In The Last Decade

J.S. Yuan

224 papers receiving 2.5k citations

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
J.S. Yuan United States	26	1.9k	309	261	228	165	240	2.6k
Cheng‐Shang Chang Taiwan	30	2.5k 1.3×	370 1.2×	164 0.6×	144 0.6×	58 0.4×	177	4.4k
Frank Liu United States	28	1.6k 0.8×	679 2.2×	114 0.4×	104 0.5×	93 0.6×	121	2.4k
Hirotaka Tamura Japan	23	1.6k 0.9×	177 0.6×	351 1.3×	547 2.4×	216 1.3×	149	2.2k
Hans Jürgen Mattausch Japan	21	1.4k 0.7×	300 1.0×	188 0.7×	172 0.8×	81 0.5×	247	1.9k
Paul Bogdan United States	33	1000 0.5×	899 2.9×	376 1.4×	310 1.4×	58 0.4×	184	3.4k
Ralph K. Cavin United States	26	1.8k 0.9×	214 0.7×	220 0.8×	297 1.3×	43 0.3×	111	2.7k
Hanho Lee South Korea	22	824 0.4×	128 0.4×	588 2.3×	112 0.5×	45 0.3×	161	1.6k
A. Thanailakis Greece	22	827 0.4×	138 0.4×	133 0.5×	183 0.8×	30 0.2×	132	1.6k
Paul D. Franzon United States	26	3.5k 1.8×	934 3.0×	287 1.1×	756 3.3×	28 0.2×	304	4.5k
Mark G. Karpovsky United States	26	1.1k 0.6×	850 2.8×	869 3.3×	50 0.2×	283 1.7×	124	2.6k

Countries citing papers authored by J.S. Yuan

Since Specialization

Citations

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

Fields of papers citing papers by J.S. Yuan

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

This network shows the impact of papers produced by J.S. Yuan. 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 J.S. Yuan. The network helps show where J.S. Yuan may publish in the future.

Co-authorship network of co-authors of J.S. Yuan

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

All Works

Sort: Min cites: Since: Top N: Style:

20 of 20 papers shown

Cai, Xiaolin, Zhixiang Pan, Rui Li, et al.. (2025). Switchable Z-scheme charge transfer and tunable electronic properties in ZnI₂/In₂Se₃ bilayer ferroelectric heterostructure. Chinese Journal of Physics. 98. 850–860.

Yuan, J.S., et al.. (2025). KITE-DDI: A Knowledge Graph Integrated Transformer Model for Accurately Predicting Drug-Drug Interaction Events From Drug SMILES and Biomedical Knowledge Graph. IEEE Access. 13. 40028–40043. 1 indexed citations

Ahmed, Waleed, et al.. (2024). Re-Evaluating Deep Learning Attacks and Defenses in Cybersecurity Systems. Big Data and Cognitive Computing. 8(12). 191–191. 2 indexed citations

Yuan, J.S., et al.. (2024). Impacting Robustness in Deep Learning-Based NIDS through Poisoning Attacks. Algorithms. 17(4). 155–155. 3 indexed citations

Arshadi, Arash Keshavarzi, et al.. (2024). Functional microRNA-targeting drug discovery by graph-based deep learning. Patterns. 5(1). 100909–100909. 3 indexed citations

Salem, Milad, Arash Keshavarzi Arshadi, & J.S. Yuan. (2022). AMPDeep: hemolytic activity prediction of antimicrobial peptides using transfer learning. BMC Bioinformatics. 23(1). 389–389. 47 indexed citations

Arshadi, Arash Keshavarzi, et al.. (2022). MolData, a molecular benchmark for disease and target based machine learning. Journal of Cheminformatics. 14(1). 10–10. 21 indexed citations

Shojaei, Alireza, et al.. (2021). Automated Machine Learning Pipeline for Traffic Count Prediction. SHILAP Revista de lepidopterología. 2(4). 482–513. 3 indexed citations

Khormali, Aminollah, et al.. (2020). Developing a Robust Defensive System against Adversarial Examples Using Generative Adversarial Networks. Big Data and Cognitive Computing. 4(2). 11–11. 11 indexed citations

10.

Arshadi, Arash Keshavarzi, Milad Salem, Jennifer Collins, et al.. (2020). Artificial Intelligence for COVID-19 Drug Discovery and Vaccine Development. Frontiers in Artificial Intelligence. 3. 65–65. 144 indexed citations

11.

Salem, Milad, et al.. (2019). RazorNet: Adversarial Training and Noise Training on a Deep Neural Network Fooled by a Shallow Neural Network. Big Data and Cognitive Computing. 3(3). 43–43. 5 indexed citations

12.

Yuan, J.S., et al.. (2019). Low-Side GaN Power Device Dynamic R_on Characteristics Under Different Substrate Biases. 1–7. 11 indexed citations

13.

Yuan, J.S., et al.. (2019). Experimental Evaluation of Time-Dependent Dielectric Breakdown for GaN MIS HEMTs under Various Substrate Biases. 277–280. 5 indexed citations

14.

Yuan, J.S., et al.. (2018). A Cross-Layer Biometric Recognition System for Mobile IoT Devices. Electronics. 7(2). 26–26. 10 indexed citations

15.

Salem, Milad, et al.. (2018). An Experimental Evaluation of Fault Diagnosis from Imbalanced and Incomplete Data for Smart Semiconductor Manufacturing. Big Data and Cognitive Computing. 2(4). 30–30. 21 indexed citations

16.

Lin, Jie, et al.. (2017). Security Interrogation and Defense for SAR Analog to Digital Converter. Electronics. 6(2). 48–48. 7 indexed citations

17.

Yuan, J.S., et al.. (2017). Ultra-Low-Power Design and Hardware Security Using Emerging Technologies for Internet of Things. Electronics. 6(3). 67–67. 22 indexed citations

18.

Yuan, J.S., et al.. (2017). Mixed-Signal Hardware Security: Attacks and Countermeasures for ΔΣ ADC. Electronics. 6(3). 60–60. 2 indexed citations

19.

Yuan, J.S., et al.. (2014). Hot Electron Effect on FinFET RF Circuit Reliability. 2(1). 1 indexed citations

20.

Li, Wei, et al.. (2000). An improved substrate current model for deep submicron MOSFETs. Solid-State Electronics. 44(11). 1985–1988. 9 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.

Explore authors with similar magnitude of impact