Yasha Wang

4.9k total citations · 2 hit papers
92 papers, 3.4k citations indexed

About

Yasha Wang is a scholar working on Artificial Intelligence, Transportation and Computer Science Applications. According to data from OpenAlex, Yasha Wang has authored 92 papers receiving a total of 3.4k indexed citations (citations by other indexed papers that have themselves been cited), including 41 papers in Artificial Intelligence, 24 papers in Transportation and 21 papers in Computer Science Applications. Recurrent topics in Yasha Wang's work include Human Mobility and Location-Based Analysis (23 papers), Machine Learning in Healthcare (23 papers) and Mobile Crowdsensing and Crowdsourcing (21 papers). Yasha Wang is often cited by papers focused on Human Mobility and Location-Based Analysis (23 papers), Machine Learning in Healthcare (23 papers) and Mobile Crowdsensing and Crowdsourcing (21 papers). Yasha Wang collaborates with scholars based in China, United Kingdom and United States. Yasha Wang's co-authors include Daqing Zhang, Jiangtao Wang, Leye Wang, Chao Chen, Junyi Ma, Shengjie Li, Yuxiang Wang, Hao Wang, Bing Xie and Liantao Ma and has published in prestigious journals such as IEEE Communications Magazine, Sensors and Computer.

In The Last Decade

Yasha Wang

86 papers receiving 3.4k citations

Hit Papers

RT-Fall: A Real-Time and Contactless Fall Detection Syste... 2016 2026 2019 2022 2016 2016 100 200 300 400

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Yasha Wang China 30 1.4k 920 859 841 636 92 3.4k
Leye Wang China 34 1.1k 0.8× 1.7k 1.8× 1.0k 1.2× 1.7k 2.0× 567 0.9× 98 4.0k
Yunhao Liu China 38 1.7k 1.2× 580 0.6× 824 1.0× 428 0.5× 2.4k 3.7× 182 4.4k
Burak Kantarcı Canada 39 1.8k 1.4× 1.2k 1.3× 1.8k 2.1× 707 0.8× 2.8k 4.3× 295 6.1k
Haoyi Xiong China 33 709 0.5× 955 1.0× 939 1.1× 718 0.9× 538 0.8× 150 3.4k
Lü Su United States 41 1.4k 1.0× 2.4k 2.6× 3.0k 3.5× 880 1.0× 961 1.5× 179 6.5k
Luca Foschini Italy 33 1.1k 0.8× 1.0k 1.1× 722 0.8× 816 1.0× 2.6k 4.2× 281 4.9k
Jizhong Zhao China 30 2.0k 1.4× 395 0.4× 467 0.5× 244 0.3× 1.3k 2.0× 158 3.5k
Hojung Cha South Korea 30 2.4k 1.7× 548 0.6× 242 0.3× 664 0.8× 1.6k 2.6× 180 3.7k
Feng Zhao China 23 1.5k 1.1× 265 0.3× 458 0.5× 299 0.4× 677 1.1× 111 2.8k
Jakob Eriksson United States 17 1.1k 0.8× 611 0.7× 350 0.4× 813 1.0× 1.1k 1.7× 35 3.2k

Countries citing papers authored by Yasha Wang

Since Specialization
Citations

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

Fields of papers citing papers by Yasha Wang

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Yasha Wang

This figure shows the co-authorship network connecting the top 25 collaborators of Yasha Wang. A scholar is included among the top collaborators of Yasha Wang 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 Yasha Wang. Yasha Wang is excluded from the visualization to improve readability, since they are connected to all nodes in the network.

All Works

20 of 20 papers shown
1.
Yang, Lin, et al.. (2025). LoRA dropout as a sparsity regularizer for overfitting reduction. Knowledge-Based Systems. 329. 114241–114241. 1 indexed citations
2.
Zhu, Yinghao, et al.. (2025). Adaptive Activation Steering: A Tuning-Free LLM Truthfulness Improvement Method for Diverse Hallucinations Categories. Edinburgh Research Explorer (University of Edinburgh). 2562–2578.
3.
Wang, Yasha, et al.. (2025). Privacy-Preserving Federated Learning Framework for Multi-Source Electronic Health Records Prognosis Prediction. Sensors. 25(8). 2374–2374. 5 indexed citations
4.
Zhu, Yinghao, Xiaochen Zheng, Wen Tang, et al.. (2025). ColaCare: Enhancing Electronic Health Record Modeling through Large Language Model-Driven Multi-Agent Collaboration. Edinburgh Research Explorer (University of Edinburgh). 2250–2261. 3 indexed citations
5.
Gao, Junyi, Yinghao Zhu, Guiying Dong, et al.. (2024). A comprehensive benchmark for COVID-19 predictive modeling using electronic health records in intensive care. Patterns. 5(4). 100951–100951. 9 indexed citations
6.
Chu, Xu, et al.. (2024). ProtoMix: Augmenting Health Status Representation Learning via Prototype-based Mixup. 3633–3644. 3 indexed citations
7.
Ma, Liantao, Junyi Gao, Xianfeng Jiao, et al.. (2023). Mortality prediction with adaptive feature importance recalibration for peritoneal dialysis patients. Patterns. 4(12). 100892–100892. 11 indexed citations
8.
Zhu, Hong, Zhisheng Liang, Shengzhi Sun, et al.. (2023). Associations Between Hourly Ambient Particulate Matter Air Pollution and Ambulance Emergency Calls: Time-Stratified Case-Crossover Study. JMIR Public Health and Surveillance. 9. e47022–e47022. 1 indexed citations
9.
Ma, Junyi, et al.. (2022). Enhancing Online Epidemic Supervising System by Compartmental and GRU Fusion Model. Mobile Information Systems. 2022. 1–15. 3 indexed citations
10.
Wang, Jiangtao, et al.. (2022). Spatial-Attention and Demographic-Augmented Generative Adversarial Imputation Network for Population Health Data Reconstruction. IEEE Transactions on Big Data. 9(4). 1057–1070. 3 indexed citations
11.
Wang, Jiangtao, et al.. (2022). Towards Sustainable Compressive Population Health: A GAN-based Year-By-Year Imputation Method. Pure (Coventry University). 4(1). 1–18. 5 indexed citations
12.
Wang, Jiangtao, Yasha Wang, & Qin Lv. (2019). Crowd-Assisted Machine Learning: Current Issues and Future Directions. Computer. 52(1). 46–53. 8 indexed citations
13.
Wang, Jiangtao, Yasha Wang, Daqing Zhang, et al.. (2018). Multi-Task Allocation in Mobile Crowd Sensing with Individual Task Quality Assurance. IEEE Transactions on Mobile Computing. 17(9). 2101–2113. 130 indexed citations
14.
Wang, Jiangtao, Yasha Wang, Daqing Zhang, et al.. (2018). Learning-Assisted Optimization in Mobile Crowd Sensing: A Survey. IEEE Transactions on Industrial Informatics. 15(1). 15–22. 29 indexed citations
15.
Wang, Jiangtao, Leye Wang, Yasha Wang, Daqing Zhang, & Linghe Kong. (2018). Task Allocation in Mobile Crowd Sensing: State-of-the-Art and Future Opportunities. IEEE Internet of Things Journal. 5(5). 3747–3757. 118 indexed citations
16.
Wang, Jiangtao, et al.. (2018). Social-Network-Assisted Worker Recruitment in Mobile Crowd Sensing. IEEE Transactions on Mobile Computing. 18(7). 1661–1673. 92 indexed citations
17.
Wang, Jiangtao, et al.. (2018). Allocating Heterogeneous Tasks in Participatory Sensing with Diverse Participant-Side Factors. IEEE Transactions on Mobile Computing. 18(9). 1979–1991. 38 indexed citations
18.
Wang, Hao, Daqing Zhang, Yasha Wang, et al.. (2016). RT-Fall: A Real-Time and Contactless Fall Detection System with Commodity WiFi Devices. IEEE Transactions on Mobile Computing. 16(2). 511–526. 491 indexed citations breakdown →
19.
Wang, Jiangtao, Yasha Wang, Daqing Zhang, et al.. (2016). Fine-Grained Multitask Allocation for Participatory Sensing With a Shared Budget. IEEE Internet of Things Journal. 3(6). 1395–1405. 57 indexed citations
20.
Wang, Jiangtao, Yasha Wang, & Yafei Wang. (2016). CAPFF: A Context-Aware Assistant for Paper Form Filling. IEEE Transactions on Human-Machine Systems. 47(6). 903–908. 3 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

Rankless by CCL
2026