Simon See

3.0k total citations · 1 hit paper
129 papers, 1.4k citations indexed

About

Simon See is a scholar working on Artificial Intelligence, Computer Networks and Communications and Computer Vision and Pattern Recognition. According to data from OpenAlex, Simon See has authored 129 papers receiving a total of 1.4k indexed citations (citations by other indexed papers that have themselves been cited), including 44 papers in Artificial Intelligence, 38 papers in Computer Networks and Communications and 37 papers in Computer Vision and Pattern Recognition. Recurrent topics in Simon See's work include Distributed and Parallel Computing Systems (25 papers), Parallel Computing and Optimization Techniques (18 papers) and Topic Modeling (12 papers). Simon See is often cited by papers focused on Distributed and Parallel Computing Systems (25 papers), Parallel Computing and Optimization Techniques (18 papers) and Topic Modeling (12 papers). Simon See collaborates with scholars based in Singapore, China and United Kingdom. Simon See's co-authors include Jianxiong Yin, Eugene Ch’ng, Lei Ma, Jianjun Zhao, Felix Juefei-Xu, Yang Liu, Xiaofei Xie, Minhui Xue, Hongxu Chen and Bo Li and has published in prestigious journals such as SHILAP Revista de lepidopterología, IEEE Transactions on Pattern Analysis and Machine Intelligence and Scientific Reports.

In The Last Decade

Simon See

114 papers receiving 1.3k citations

Hit Papers

align trajectories

What are hit papers?

Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the same subfield and year (this is the minimum needed to enter the top 1%, not the average within it), or reaches the top citation threshold in at least one of its specific research topics.

DeepHunter: a coverage-guided fuzz testing framework for deep neural networks

2019 269 citations Xiaofei Xie, Lei Ma et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Simon See Singapore	20	534	364	316	244	149	129	1.4k
Kang Zhang United States	20	305 0.6×	404 1.1×	151 0.5×	371 1.5×	156 1.0×	153	1.3k
Dieter Kranzlmüller Germany	20	189 0.4×	236 0.6×	529 1.7×	205 0.8×	41 0.3×	147	1.4k
Bo Wu United States	23	634 1.2×	711 2.0×	363 1.1×	309 1.3×	22 0.1×	90	1.6k
Aimilia Tzanavari Cyprus	8	254 0.5×	148 0.4×	165 0.5×	144 0.6×	41 0.3×	16	815
Lian Li China	21	308 0.6×	125 0.3×	377 1.2×	299 1.2×	180 1.2×	172	1.4k
Ken Arnold United Kingdom	13	663 1.2×	160 0.4×	773 2.4×	480 2.0×	196 1.3×	48	1.7k
宏治津田 Japan	1	1.1k 2.0×	867 2.4×	142 0.4×	172 0.7×	37 0.2×	2	2.0k
Charles Nicholas United States	17	703 1.3×	166 0.5×	321 1.0×	516 2.1×	57 0.4×	70	1.4k
William F. Clocksin United Kingdom	18	1.0k 1.9×	437 1.2×	323 1.0×	213 0.9×	198 1.3×	53	1.9k

Countries citing papers authored by Simon See

Since Specialization

Citations

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

Fields of papers citing papers by Simon See

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Simon See

This figure shows the co-authorship network connecting the top 25 collaborators of Simon See. A scholar is included among the top collaborators of Simon See 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 Simon See. Simon See 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

Eden, Jonathan, et al.. (2025). Leveraging physics-informed neural networks for efficient modelling of coastal ecosystems dynamics: A case study of Sundarbans mangrove forest. Ecological Informatics. 90. 103302–103302.

See, Simon, et al.. (2025). Validating large-scale quantum machine learning: efficient simulation of quantum support vector machines using tensor networks. Machine Learning Science and Technology. 6(1). 15047–15047. 8 indexed citations

Liu, Dikai, Jianxiong Yin, & Simon See. (2024). Towards Fault-tolerant Quadruped Locomotion with Reinforcement Learning. 1438–1441. 3 indexed citations

Huang, Lihui, S Li, Nanying Liang, et al.. (2024). Designing an Educational Metaverse: A Case Study of NTUniverse. Applied Sciences. 14(6). 2559–2559. 10 indexed citations

Song, Jiayang, Xuan Xie, Zhan Shu, et al.. (2024). Towards Building AI-CPS with NVIDIA Isaac Sim: An Industrial Benchmark and Case Study for Robotics Manipulation. 263–274. 9 indexed citations

Wang, Jun, et al.. (2024). CAMANet: Class Activation Map Guided Attention Network for Radiology Report Generation. IEEE Journal of Biomedical and Health Informatics. 28(4). 2199–2210. 18 indexed citations

Low, Malcolm Yoke Hean, et al.. (2024). Towards Accelerating Physics Informed Graph Neural Network for Fluid Simulation. 1–3. 1 indexed citations

Mehmood, Rashid, Iyad Katib, Xiaohui Liang, et al.. (2024). Harmonizing AI governance regulations and neuroinformatics: perspectives on privacy and data sharing. Frontiers in Neuroinformatics. 18. 1472653–1472653. 3 indexed citations

Tan, Chek Tien, et al.. (2023). SynthDa: Exploiting Existing Real-World Data for Usable and Accessible Synthetic Data Generation. Figshare. 1–4. 1 indexed citations

10.

Zhu, Lanyun, Tianrun Chen, Jianxiong Yin, Simon See, & Jun Liu. (2023). Learning Gabor Texture Features for Fine-Grained Recognition. 1621–1631. 21 indexed citations

11.

Cheung, Ka Chun, et al.. (2023). Transfer Learning With Singular Value Decomposition of Multichannel Convolution Matrices. Neural Computation. 35(10). 1678–1712.

12.

Liu, Timothy, et al.. (2023). NewsMet : A ‘do it all’ Dataset of Contemporary Metaphors in News Headlines. 10090–10104. 1 indexed citations

13.

Zhang, Hongming, et al.. (2022). PseudoReasoner: Leveraging Pseudo Labels for Commonsense Knowledge Base Population. 3379–3394. 3 indexed citations

14.

Liu, Xin, et al.. (2022). Complex Hyperbolic Knowledge Graph Embeddings with Fast Fourier Transform. Rare & Special e-Zone (The Hong Kong University of Science and Technology). 5228–5239. 6 indexed citations

15.

Xie, Xiaofei, Lei Ma, Felix Juefei-Xu, et al.. (2018). Coverage-Guided Fuzzing for Deep Neural Networks.. arXiv (Cornell University). 18 indexed citations

16.

Wang, Bingqiang & Simon See. (2012). Accelerate high throughput analysis for genome sequencing with GPU. IEEE International Conference on High Performance Computing, Data, and Analytics. 11. 1 indexed citations

17.

March, Verdi, et al.. (2010). On Parallel Stiff ODEs Solver for Hybrid CPU-GPU Architecture. 365–368. 1 indexed citations

18.

Li, Hua, Shantanu S. Mulay, & Simon See. (2009). On the Location of Zeroes of Polynomials from the Stability Analysis of Novel Strong-Form Meshless Random Differential Quadrature Method. Computer Modeling in Engineering & Sciences. 54(2). 147–200. 5 indexed citations

19.

Li, Hua, Shantanu S. Mulay, & Simon See. (2009). On the Convergence of Random Differential Quadrature (RDQ) Method and Its Application in Solving Nonlinear Differential Equations in Mechanics. Computer Modeling in Engineering & Sciences. 48(1). 43–82. 5 indexed citations

20.

Wang, Lizhe, Wentong Cai, Bu‐Sung Lee, Simon See, & Wei Jie. (2003). Resource Co-Allocation for Meta-Task in Computational Grids.. Parallel and Distributed Processing Techniques and Applications. 36–42. 2 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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