Junru Shao

976 total citations · 1 hit paper
8 papers, 444 citations indexed

About

Junru Shao is a scholar working on Computer Vision and Pattern Recognition, Hardware and Architecture and Artificial Intelligence. According to data from OpenAlex, Junru Shao has authored 8 papers receiving a total of 444 indexed citations (citations by other indexed papers that have themselves been cited), including 5 papers in Computer Vision and Pattern Recognition, 4 papers in Hardware and Architecture and 4 papers in Artificial Intelligence. Recurrent topics in Junru Shao's work include Parallel Computing and Optimization Techniques (4 papers), Tensor decomposition and applications (3 papers) and Advanced Image and Video Retrieval Techniques (2 papers). Junru Shao is often cited by papers focused on Parallel Computing and Optimization Techniques (4 papers), Tensor decomposition and applications (3 papers) and Advanced Image and Video Retrieval Techniques (2 papers). Junru Shao collaborates with scholars based in United States and China. Junru Shao's co-authors include Claire Cardie, Xinya Du, Tianqi Chen, Zihao Ye, Luís Ceze, Lianmin Zheng, Cody Hao Yu, Siyuan Feng, Shicong Liu and Hongtao Lu and has published in prestigious journals such as IEEE Transactions on Multimedia, Neural Information Processing Systems and International Conference on Artificial Intelligence and Statistics.

In The Last Decade

Junru Shao

8 papers receiving 412 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.

Learning to Ask: Neural Question Generation for Reading Comprehension

2017 336 citations Xinya Du, Junru Shao et al. profile →

Peers — A (Enhanced Table)

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

Name	h						Papers	Cites
Junru Shao United States	6	367	180	59	54	17	8	444
Horace He United States	3	305 0.8×	87 0.5×	45 0.8×	32 0.6×	6 0.4×	5	382
Benoit Steiner United States	4	83 0.2×	105 0.6×	34 0.6×	121 2.2×	23 1.4×	5	213
Pengcheng Yao China	11	110 0.3×	165 0.9×	57 1.0×	97 1.8×	4 0.2×	25	273
Bairen Yi Hong Kong	6	159 0.4×	122 0.7×	179 3.0×	68 1.3×	9 0.5×	6	397
Sharan Narang United States	7	192 0.5×	104 0.6×	21 0.4×	8 0.1×	2 0.1×	7	269
Stefan Lucks Germany	8	228 0.6×	143 0.8×	81 1.4×	34 0.6×		37	310
Ruigang Liang China	7	124 0.3×	53 0.3×	43 0.7×	20 0.4×		21	200
Seira Hidano Japan	9	127 0.3×	38 0.2×	42 0.7×	64 1.2×		43	204
Keiichi Iwamura Japan	9	161 0.4×	106 0.6×	94 1.6×	46 0.9×		86	295

Countries citing papers authored by Junru Shao

Since Specialization

Citations

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

Fields of papers citing papers by Junru Shao

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Junru Shao

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

8 of 8 papers shown

1.

Shao, Junru, Zihao Ye, Jiawei Liu, et al.. (2025). Relax: Composable Abstractions for End-to-End Dynamic Machine Learning. 998–1013. 1 indexed citations

2.

Ye, Zihao, et al.. (2023). SparseTIR: Composable Abstractions for Sparse Compilation in Deep Learning. 660–678. 48 indexed citations

3.

Feng, Siyuan, Junru Shao, Zihao Ye, et al.. (2023). TensorIR: An Abstraction for Automatic Tensorized Program Optimization. 804–817. 34 indexed citations

4.

Zheng, Lianmin, Ruochen Liu, Junru Shao, et al.. (2021). TenSet: A Large-scale Program Performance Dataset for Learned Tensor Compilers. Neural Information Processing Systems. 8 indexed citations

5.

Zhang, Hongyang, Junru Shao, & Ruslan Salakhutdinov. (2019). Deep Neural Networks with Multi-Branch Architectures Are Intrinsically Less Non-Convex. International Conference on Artificial Intelligence and Statistics. 1099–1109. 7 indexed citations

6.

Du, Xinya, Junru Shao, & Claire Cardie. (2017). Learning to Ask: Neural Question Generation for Reading Comprehension. 1342–1352. 336 indexed citations breakdown →

7.

Liu, Shicong, Junru Shao, & Hongtao Lu. (2017). Generalized Residual Vector Quantization and Aggregating Tree for Large Scale Search. IEEE Transactions on Multimedia. 19(8). 1785–1797. 9 indexed citations

8.

Liu, Shicong, Junru Shao, & Hongtao Lu. (2016). Generalized residual vector quantization for large scale data. 1. 1–6. 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.

Explore authors with similar magnitude of impact