Shuji Hao

2.7k total citations · 1 hit paper
12 papers, 1.5k citations indexed

About

Shuji Hao is a scholar working on Artificial Intelligence, Computer Networks and Communications and Computer Vision and Pattern Recognition. According to data from OpenAlex, Shuji Hao has authored 12 papers receiving a total of 1.5k indexed citations (citations by other indexed papers that have themselves been cited), including 10 papers in Artificial Intelligence, 3 papers in Computer Networks and Communications and 3 papers in Computer Vision and Pattern Recognition. Recurrent topics in Shuji Hao's work include Machine Learning and Algorithms (5 papers), Data Stream Mining Techniques (4 papers) and Domain Adaptation and Few-Shot Learning (3 papers). Shuji Hao is often cited by papers focused on Machine Learning and Algorithms (5 papers), Data Stream Mining Techniques (4 papers) and Domain Adaptation and Few-Shot Learning (3 papers). Shuji Hao collaborates with scholars based in Singapore and China. Shuji Hao's co-authors include Jindong Wang, Xiaohui Peng, Lisha Hu, Yiqiang Chen, Chunyan Miao, Peilin Zhao, Steven C. H. Hoi, Wei Wang, Bu‐Sung Lee and Jing Lu and has published in prestigious journals such as IEEE Transactions on Knowledge and Data Engineering, Machine Learning and Pattern Recognition Letters.

In The Last Decade

Shuji Hao

11 papers receiving 1.4k 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.

Deep learning for sensor-based activity recognition: A survey

2018 1.4k citations Jindong Wang, Yiqiang Chen et al. Pattern Recognition Letters profile →

Peers

Shuji Hao

CVPR

AI

BE

CNC

EEE

Miguel Damas Spain

Francisco Ordóñez United Kingdom

Gwenn Englebienne Netherlands

Xavier Parra Spain

Sozo Inoue Japan

Charissa Ann Ronao South Korea

Gerald Pirkl Germany

Kilian Förster Switzerland

Nishkam Ravi United States

Ming Zeng China

Miguel Damas Spain

Shuji Hao

859 ×0.9

CVPR

425 ×0.8

AI

514 ×1.2

BE

324 ×1.0

CNC

264 ×1.4

EEE

Citations per year, relative to Shuji Hao Shuji Hao (= 1×) peers Miguel Damas

Countries citing papers authored by Shuji Hao

Since Specialization

Citations

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

Fields of papers citing papers by Shuji Hao

Since Specialization

Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Shuji Hao

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

12 of 12 papers shown

1.

Ni, Rui, et al.. (2025). Short-term Prediction of Ionospheric TEC Based on SSA-BP and LSTM. 246–249.

2.

Li, Qianxiao & Shuji Hao. (2018). An Optimal Control Approach to Deep Learning and Applications to Discrete-Weight Neural Networks. International Conference on Machine Learning. 2985–2994. 4 indexed citations

3.

Wang, Jindong, Yiqiang Chen, Shuji Hao, Xiaohui Peng, & Lisha Hu. (2018). Deep learning for sensor-based activity recognition: A survey. Pattern Recognition Letters. 119. 3–11. 1380 indexed citations breakdown →

4.

Hao, Shuji, et al.. (2018). Online Active Learning with Expert Advice. ACM Transactions on Knowledge Discovery from Data. 12(5). 1–22. 11 indexed citations

5.

Zhang, Chi, Peilin Zhao, Shuji Hao, et al.. (2017). Distributed multi-task classification: a decentralized online learning approach. Machine Learning. 107(4). 727–747. 21 indexed citations

6.

Hao, Shuji, Jing Lu, Peilin Zhao, et al.. (2017). Second-Order Online Active Learning and Its Applications. IEEE Transactions on Knowledge and Data Engineering. 30(7). 1338–1351. 23 indexed citations

7.

Wang, Wei, Chunyan Miao, & Shuji Hao. (2017). Zero-shot human activity recognition via nonlinear compatibility based method. 322–330. 16 indexed citations

8.

Hao, Shuji, Peilin Zhao, Yong Liu, Steven C. H. Hoi, & Chunyan Miao. (2017). Online Multitask Relative Similarity Learning. 1823–1829. 8 indexed citations

9.

Zhang, Chi, Peilin Zhao, Shuji Hao, Yeng Chai Soh, & Bu‐Sung Lee. (2016). ROM: A Robust Online Multi-task Learning Approach. 1341–1346. 4 indexed citations

10.

Hao, Shuji, Peilin Zhao, Jing Lü, et al.. (2016). SOAL: Second-Order Online Active Learning. 931–936. 11 indexed citations

11.

Hao, Shuji, Steven C. H. Hoi, Chunyan Miao, & Peilin Zhao. (2015). Active Crowdsourcing for Annotation. 2. 1–8. 8 indexed citations

12.

Hao, Shuji, Peilin Zhao, Steven C. H. Hoi, & Chunyan Miao. (2015). Learning Relative Similarity from Data Streams. Institutional Knowledge (InK) - Institutional Knowledge at Singapore Management University (Singapore Management University). 1181–1190. 6 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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