Sinan Wang

1.1k citations
43 papers · 789 · h-index 14

Impact in

Papers in

Sinan Wang

38 papers receiving 765 citations

Peers

Sinan Wang
Comparison fields: 5 of 93
  • Global and Planetary Change 162
  • Computer Vision and Pattern Recognition 163
  • Automotive Engineering 88
  • Artificial Intelligence 238
  • Renewable Energy, Sustainability and the Environment 110
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Citations per field
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Citations per year

Countries citing papers authored by Sinan Wang

Since Specialization
Citations

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

Fields of papers citing papers by Sinan Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authors

The 25 scholars most cited alongside Sinan Wang, 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 Sinan Wang Line = papers co-authored together Sinan Wang links everyone, so they are left out of the graph.

All Works

20 of 20 papers shown

Showing the 20 most-cited of 43 papers — load more, or switch the sort, to bring in the rest.

#Work
1
Transferability vs. Discriminability: Batch Spectral Penalization for Adversarial Domain Adaptation
2019170
2 202274
3
Catastrophic Forgetting Meets Negative Transfer: Batch Spectral Shrinkage for Safe Transfer Learning
201969
4 202347
5 201845
6 202239
7 202239
8 201839
9 202037
10 201534
11 201931
12 202227
13 201723
14 202318
15 202114
16 20239
17 20229
18 20239
19 20248
20 20225

About Sinan Wang

Sinan Wang is a scholar working on Global and Planetary Change, Water Science and Technology, Atmospheric Science, Renewable Energy, Sustainability and the Environment and Management, Monitoring, Policy and Law, having authored 43 papers that have together received 789 indexed citations. Recurring topics across this work include Plant Water Relations and Carbon Dynamics (12 papers), Hydrology and Drought Analysis (11 papers), Climate variability and models (10 papers), Hydrology and Watershed Management Studies (9 papers), Remote Sensing in Agriculture (7 papers), Energy, Environment, and Transportation Policies (5 papers), Electric Vehicles and Infrastructure (5 papers) and Tree-ring climate responses (4 papers). The work is most often cited by research in Global and Planetary Change (162 citations), Computer Vision and Pattern Recognition (163 citations), Automotive Engineering (88 citations), Artificial Intelligence (238 citations) and Renewable Energy, Sustainability and the Environment (110 citations). Sinan Wang has collaborated with scholars based in China, United States and Singapore. Frequent co-authors include Jianmin Wang, Yingjie Wu, Xinyang Chen, Mingsheng Long, Ruiping Li, Zongwei Liu, Fuquan Zhao, Han Hao, Wenjun Wang and Kangda Chen. Their work appears in journals such as Scientific Reports, Agronomy, Ecological Indicators, Global Ecology and Conservation and Energy.

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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