Pengming Wang

525 total citations · 1 hit paper
17 papers, 186 citations indexed

About

Pengming Wang is a scholar working on Artificial Intelligence, Computational Theory and Mathematics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Pengming Wang has authored 17 papers receiving a total of 186 indexed citations (citations by other indexed papers that have themselves been cited), including 7 papers in Artificial Intelligence, 6 papers in Computational Theory and Mathematics and 2 papers in Computer Vision and Pattern Recognition. Recurrent topics in Pengming Wang's work include Advanced Graph Theory Research (3 papers), Complexity and Algorithms in Graphs (3 papers) and Polynomial and algebraic computation (2 papers). Pengming Wang is often cited by papers focused on Advanced Graph Theory Research (3 papers), Complexity and Algorithms in Graphs (3 papers) and Polynomial and algebraic computation (2 papers). Pengming Wang collaborates with scholars based in China, United Kingdom and Germany. Pengming Wang's co-authors include Alexander Novikov, Manish Kumar, Omar Fawzi, Mohammadamin Barekatain, Emilien Dupont, Francisco J. R. Ruiz, Bernardino Romera‐Paredes, Jordan S. Ellenberg, Matej Balog and Alhussein Fawzi and has published in prestigious journals such as Nature, Neural Computing and Applications and Journal of Computer Assisted Tomography.

In The Last Decade

Pengming Wang

15 papers receiving 176 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.

Mathematical discoveries from program search with large language models

2023 124 citations Bernardino Romera‐Paredes, Mohammadamin Barekatain et al. Nature profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Pengming Wang China	6	67	42	17	14	13	17	186
Anthony Lins Brazil	5	98 1.5×	34 0.8×	2 0.1×	17 1.2×	20 1.5×	15	190
David Harlan Wood United States	7	112 1.7×	44 1.0×	5 0.3×	9 0.6×	36 2.8×	20	204
Yasuyoshi Inagaki Japan	9	199 3.0×	68 1.6×	13 0.8×	15 1.1×	29 2.2×	45	276
Jason Wang China	7	89 1.3×	6 0.1×	13 0.8×	8 0.6×	42 3.2×	19	177
Jayson Lynch United States	4	62 0.9×	11 0.3×	2 0.1×	13 0.9×	9 0.7×	27	114
Stefano Melacci Italy	9	133 2.0×	15 0.4×	14 0.8×	19 1.4×	93 7.2×	38	232
Timothy Mann United States	8	166 2.5×	24 0.6×	3 0.2×	14 1.0×	32 2.5×	33	202
Mohamed Eldesouki Egypt	9	182 2.7×	8 0.2×	3 0.2×	26 1.9×	31 2.4×	47	277
Hongyang Zhang China	7	178 2.7×	13 0.3×	7 0.4×	8 0.6×	56 4.3×	29	227
Francisco Fernández de Vega Spain	8	91 1.4×	25 0.6×	1 0.1×	22 1.6×	29 2.2×	25	149

Countries citing papers authored by Pengming Wang

Since Specialization

Citations

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

Fields of papers citing papers by Pengming Wang

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Pengming Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Pengming Wang. A scholar is included among the top collaborators of Pengming Wang 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 Pengming Wang. Pengming Wang 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:

17 of 17 papers shown

1.

Chen, Qing, et al.. (2024). Research on biomechanics integrated Bayesian network mental health diagnosis system. Molecular & cellular biomechanics. 21(3). 545–545.

2.

Chen, Qing, et al.. (2024). Advanced Multimodal Sentiment Analysis with Enhanced Contextual Fusion and Robustness (AMSA-ECFR): Symmetry in Feature Integration and Data Alignment. Symmetry. 16(7). 934–934. 1 indexed citations

3.

Wang, Pengming, et al.. (2024). Text Reasoning Chain Extraction for Multi-Hop Question Answering. Tsinghua Science & Technology. 29(4). 959–970. 1 indexed citations

4.

Wang, Pengming, M. M. Kamruzzaman, & Qing Chen. (2023). Machine reading comprehension model based on query reconstruction technology and deep learning. Neural Computing and Applications. 36(5). 2155–2170.

5.

Romera‐Paredes, Bernardino, Mohammadamin Barekatain, Alexander Novikov, et al.. (2023). Mathematical discoveries from program search with large language models. Nature. 625(7995). 468–475. 124 indexed citations breakdown →

6.

Zhang, Lu, et al.. (2022). Value of 3.0T MRI T2 mapping combined with SWI for the assessment of early lesions in hemophilic arthropathy. Hematology. 27(1). 1263–1271. 1 indexed citations

7.

Wang, Pengming, et al.. (2022). Heterogeneous domain adaptation by semantic distribution alignment network. Applied Intelligence. 53(11). 14284–14297. 2 indexed citations

8.

Wang, Hongbin, et al.. (2022). MRFCNN: the optimisation method of convolutional neural network for underwater target recognition. International Journal of Modelling Identification and Control. 40(1). 36–36. 1 indexed citations

9.

Zheng, Yue, et al.. (2021). Abnormal brain functional networks in end‐stage renal disease patients with cognitive impairment. Brain and Behavior. 11(4). e02076–e02076. 12 indexed citations

10.

Wang, Hongbin, et al.. (2021). Improved Relief Weight Feature Selection Algorithm Based on Relief and Mutual Information. Information. 12(6). 228–228. 6 indexed citations

11.

Dawar, Anuj & Pengming Wang. (2017). Definability of semidefinite programming and lasserre lower bounds for CSPs. 1–12. 3 indexed citations

12.

Wang, Pengming, et al.. (2017). Investigation on Computed Tomography Features of Primary Thymic Atypical Carcinoid Tumors. Journal of Computer Assisted Tomography. 41(6). 990–994. 5 indexed citations

13.

Dawar, Anuj & Pengming Wang. (2017). Definability of semidefinite programming and lasserre lower bounds for CSPs. Apollo (University of Cambridge). 1–12. 3 indexed citations

14.

Dawar, Anuj & Pengming Wang. (2015). A Definability Dichotomy for Finite Valued CSPs. DROPS (Schloss Dagstuhl – Leibniz Center for Informatics). 1 indexed citations

15.

Mehlhorn, Kurt, Michael Sagraloff, & Pengming Wang. (2014). From approximate factorization to root isolation with application to cylindrical algebraic decomposition. Journal of Symbolic Computation. 66. 34–69. 19 indexed citations

16.

Mehlhorn, Kurt, Michael Sagraloff, & Pengming Wang. (2013). From approximate factorization to root isolation. 283–290. 6 indexed citations

17.

Wang, Pengming. (2002). The digitalization of industrial design. 1 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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