Teng Su

447 total citations · 1 hit paper
7 papers, 156 citations indexed

About

Teng Su is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition and Computer Networks and Communications. According to data from OpenAlex, Teng Su has authored 7 papers receiving a total of 156 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Artificial Intelligence, 2 papers in Computer Vision and Pattern Recognition and 1 paper in Computer Networks and Communications. Recurrent topics in Teng Su's work include AI in cancer detection (2 papers), Advanced Neural Network Applications (2 papers) and Radiomics and Machine Learning in Medical Imaging (1 paper). Teng Su is often cited by papers focused on AI in cancer detection (2 papers), Advanced Neural Network Applications (2 papers) and Radiomics and Machine Learning in Medical Imaging (1 paper). Teng Su collaborates with scholars based in China and Hong Kong. Teng Su's co-authors include Yuxiao Dong, Qinkai Zheng, Xu Zou, Yufei Xue, Jie Tang, Fan Yu, Zhenkun Cai, Xiao Yan, James Cheng and Yuzhen Huang and has published in prestigious journals such as IEEE Transactions on Parallel and Distributed Systems, Computer Methods and Programs in Biomedicine and Advanced materials research.

In The Last Decade

Teng Su

5 papers receiving 152 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.

CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X

2023 110 citations Qinkai Zheng, Xu Zou et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Teng Su China	4	67	60	45	25	20	7	156
Sigrid Gürgens Germany	8	81 1.2×	68 1.1×	26 0.6×	62 2.5×	6 0.3×	19	137
Arno Puder United States	8	69 1.0×	74 1.2×	23 0.5×	59 2.4×	12 0.6×	23	158
Peter C. Dillinger United States	5	47 0.7×	18 0.3×	30 0.7×	16 0.6×	24 1.2×	12	104
Jeho Oh United States	6	107 1.6×	120 2.0×	84 1.9×	75 3.0×	9 0.5×	7	176
Gudmund Grov United Kingdom	6	77 1.1×	55 0.9×	60 1.3×	36 1.4×	7 0.3×	30	143
Jiazhen Gu China	8	88 1.3×	77 1.3×	53 1.2×	100 4.0×	16 0.8×	23	210
Daoguang Zan China	6	117 1.7×	95 1.6×	66 1.5×	22 0.9×	10 0.5×	11	202
Aryaz Eghbali Germany	5	55 0.8×	110 1.8×	107 2.4×	40 1.6×	4 0.2×	7	190
Nicolas Palix Denmark	6	39 0.6×	54 0.9×	49 1.1×	53 2.1×	12 0.6×	12	102
Helmut Neukirchen Iceland	7	29 0.4×	84 1.4×	60 1.3×	47 1.9×	9 0.5×	32	136

Countries citing papers authored by Teng Su

Since Specialization

Citations

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

Fields of papers citing papers by Teng Su

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Teng Su

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

7 of 7 papers shown

1.

Yang, Mei, Lifu Zhang, Teng Su, et al.. (2025). EfficientMoE: Optimizing Mixture-of-Experts Model Training With Adaptive Load Balance. IEEE Transactions on Parallel and Distributed Systems. 36(4). 677–688. 1 indexed citations

2.

Su, Teng, Qing Yang, Meng Si, et al.. (2025). The knowledge distillation-assisted multimodal model for osteoporosis screening. Computer Methods and Programs in Biomedicine. 269. 108848–108848.

3.

Su, Teng, et al.. (2023). A Survey on Auto-Parallelism of Large-Scale Deep Learning Training. IEEE Transactions on Parallel and Distributed Systems. 34(8). 2377–2390. 14 indexed citations

4.

Zheng, Qinkai, Xu Zou, Yuxiao Dong, et al.. (2023). CodeGeeX: A Pre-Trained Model for Code Generation with Multilingual Benchmarking on HumanEval-X. 5673–5684. 110 indexed citations breakdown →

5.

Su, Teng, Yuyan Zhang, Qing Yang, & Bing Ji. (2023). A Hierarchical Opportunistic Screening Model for Osteoporosis Using Deep Learning. 8273–8278.

6.

Cai, Zhenkun, Xiao Yan, Yuzhen Huang, et al.. (2021). TensorOpt: Exploring the Tradeoffs in Distributed DNN Training With Auto-Parallelism. IEEE Transactions on Parallel and Distributed Systems. 33(8). 1967–1981. 24 indexed citations

7.

Su, Teng, et al.. (2014). Sea Oil Spill Detection Method Using SAR Imagery Combined with Object-Based Image Analysis and Fuzzy Logic. Advanced materials research. 1065-1069. 3192–3200. 7 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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