Si Si

113 papers receiving 2.6k citations

Peers

Si Si
Comparison fields: 5 of 181
  • Computational Mathematics 39
  • Computer Vision and Pattern Recognition 532
  • Artificial Intelligence 687
  • Renewable Energy, Sustainability and the Environment 291
  • Environmental Chemistry 133
Replace Shuqiang Wang with:
Shuqiang Wang China
Zhi Wang China
Amith Khandakar Qatar
Yao Hu China
Mehmet Gönen Türkiye
Zhaowen Wang China
Jiamin Liu China
Zewen Li China
Andreas W. Kempa-Liehr Germany
Si Si relative to Shuqiang Wang China Shuqiang Wang's profile →
Citations per field
00.5×10×13.3×
Shuqiang Wang · 1×
Citations per year

Countries citing papers authored by Si Si

Since Specialization
Citations

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

Fields of papers citing papers by Si Si

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Si Si, linked wherever they have co-authored with each other. Click a name or a connecting line to browse the papers they share.

Border = papers with Si Si Line = papers co-authored together Si Si links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 120 papers — load more, or switch the sort, to bring in the rest.

#Work
1 2009360
2 2017274
3 2012162
4 2013120
5 201383
6 201381
7 201472
8
Gradient boosted decision trees for high dimensional sparse output
201771
9 202162
10 201958
11 202057
12 201855
13 201349
14 201949
15 202049
16 202143
17 201437
18 201837
19 202237
20 202031

About Si Si

Si Si is a scholar working on Artificial Intelligence, Computer Vision and Pattern Recognition, Experimental and Cognitive Psychology, Electrical and Electronic Engineering and Renewable Energy, Sustainability and the Environment, having authored 120 papers that have together received 2.6k indexed citations. Recurring topics across this work include Face and Expression Recognition (10 papers), Electrocatalysts for Energy Conversion (8 papers), Stochastic processes and financial applications (8 papers), Creativity in Education and Neuroscience (8 papers), Advanced battery technologies research (7 papers), Stochastic Gradient Optimization Techniques (5 papers), Machine Learning and ELM (5 papers) and Occupational exposure and asthma (5 papers). The work is most often cited by research in Computational Mathematics (39 citations), Computer Vision and Pattern Recognition (532 citations), Artificial Intelligence (687 citations), Renewable Energy, Sustainability and the Environment (291 citations) and Environmental Chemistry (133 citations). Si Si has collaborated with scholars based in China, Australia and United States. Frequent co-authors include Inderjit S. Dhillon, Cho‐Jui Hsieh, Dacheng Tao, Bo Geng, Hsiang‐Fu Yu, Yaoguang Rong, Huawei Liu, Xiong Li, Yue Hu and Hongwei Han. Their work appears in journals such as Infinite Dimensional Analysis Quantum Probability and Related Topics, Occupational and Environmental Medicine, American Journal of Industrial Medicine, Nagoya Mathematical Journal and PLoS ONE.

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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