Rose Yu

6.6k total citations
42 papers, 638 citations indexed

About

Rose Yu is a scholar working on Artificial Intelligence, Statistical and Nonlinear Physics and Computer Vision and Pattern Recognition. According to data from OpenAlex, Rose Yu has authored 42 papers receiving a total of 638 indexed citations (citations by other indexed papers that have themselves been cited), including 15 papers in Artificial Intelligence, 11 papers in Statistical and Nonlinear Physics and 5 papers in Computer Vision and Pattern Recognition. Recurrent topics in Rose Yu's work include Model Reduction and Neural Networks (6 papers), Complex Network Analysis Techniques (5 papers) and Tensor decomposition and applications (4 papers). Rose Yu is often cited by papers focused on Model Reduction and Neural Networks (6 papers), Complex Network Analysis Techniques (5 papers) and Tensor decomposition and applications (4 papers). Rose Yu collaborates with scholars based in United States, Hong Kong and United Kingdom. Rose Yu's co-authors include Yan Liu, Xinran He, Paul Wong, Albert-Ĺaszló Barabási, Rui Wang, Anima Anandkumar, Stephan Zheng, Yisong Yue, Yan Liu and Ching‐Yung Lin and has published in prestigious journals such as Proceedings of the National Academy of Sciences, Nature Communications and PLoS ONE.

In The Last Decade

Rose Yu

41 papers receiving 614 citations

Peers — A (Enhanced Table)

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

Name h Career Trend Papers Cites
Rose Yu United States 14 216 91 83 75 69 42 638
Sheng Gao China 17 648 3.0× 68 0.7× 60 0.7× 66 0.9× 113 1.6× 91 1.0k
Chenbo Fu China 14 258 1.2× 359 3.9× 252 3.0× 77 1.0× 65 0.9× 37 725
H. Vicky Zhao China 19 219 1.0× 106 1.2× 255 3.1× 60 0.8× 233 3.4× 111 1.2k
Yifan Zhu China 19 455 2.1× 55 0.6× 80 1.0× 34 0.5× 36 0.5× 106 987
Jiachen Sun China 13 111 0.5× 88 1.0× 167 2.0× 42 0.6× 68 1.0× 37 503
Ludovic Denoyer France 15 853 3.9× 39 0.4× 51 0.6× 31 0.4× 84 1.2× 34 1.1k
Rik Sarkar United Kingdom 16 342 1.6× 184 2.0× 383 4.6× 55 0.7× 95 1.4× 33 1.1k

Countries citing papers authored by Rose Yu

Since Specialization
Citations

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

Fields of papers citing papers by Rose Yu

Since Specialization
Physical SciencesHealth SciencesLife SciencesSocial Sciences

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

Co-authorship network of co-authors of Rose Yu

This figure shows the co-authorship network connecting the top 25 collaborators of Rose Yu. A scholar is included among the top collaborators of Rose Yu 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 Rose Yu. Rose Yu 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.
Yu, Rose, et al.. (2025). Physics-Guided Deep Learning for Dynamical Systems: A Survey. ACM Computing Surveys. 58(5). 1–31. 1 indexed citations
2.
Yang, Yang, Ching‐Ting Tsai, Aayush Gupta, et al.. (2025). Intelligent in-cell electrophysiology: Reconstructing intracellular action potentials using a physics-informed deep learning model trained on nanoelectrode array recordings. Nature Communications. 16(1). 657–657. 4 indexed citations
3.
Eckmann, Peter, Jake Anderson, Rose Yu, & Michael K. Gilson. (2024). Ligand-Based Compound Activity Prediction via Few-Shot Learning. Journal of Chemical Information and Modeling. 64(14). 5492–5499. 4 indexed citations
4.
Farcas, Emilia, Job Godino, Kevin Patrick, et al.. (2024). Preliminary Validity and Acceptability of Motion Tape for Measuring Low Back Movement: Mixed Methods Study. JMIR Rehabilitation and Assistive Technologies. 11. e57953–e57953. 4 indexed citations
5.
Qian, Yinlong, et al.. (2024). Back to Bayesics: Uncovering Human Mobility Distributions and Anomalies with an Integrated Statistical and Neural Framework. eScholarship (California Digital Library). 56–67. 1 indexed citations
6.
Naranjo, Mario, Rose Yu, Allen D. Everett, et al.. (2024). Collagen 18A1/Endostatin Expression in the Progression of Right Ventricular Remodeling and Dysfunction in Pulmonary Arterial Hypertension. American Journal of Respiratory Cell and Molecular Biology. 71(3). 343–355. 2 indexed citations
7.
Krenn, Mario, Lorenzo Buffoni, Bruno Coutinho, et al.. (2023). Forecasting the future of artificial intelligence with machine learning-based link prediction in an exponentially growing knowledge network. Nature Machine Intelligence. 5(11). 1326–1335. 36 indexed citations
8.
Yu, Rose, et al.. (2023). Accelerating network layouts using graph neural networks. Nature Communications. 14(1). 1560–1560. 10 indexed citations
9.
Wu, Dongxia, et al.. (2023). Deep Bayesian Active Learning for Accelerating Stochastic Simulation. 2559–2569. 2 indexed citations
10.
11.
Zhang, Chi, et al.. (2020). Learning Disentangled Representations of Videos with Missing Data. arXiv (Cornell University). 33. 3625–3635. 5 indexed citations
12.
Barabási, Albert-Ĺaszló, et al.. (2019). Understanding the Representation Power of Graph Neural Networks in Learning Graph Topology. Neural Information Processing Systems. 32. 15413–15423. 14 indexed citations
13.
Kashinath, Karthik, et al.. (2019). Physics-informed Spatio-temporal Deep Learning Models. Bulletin of the American Physical Society. 2 indexed citations
14.
Wong, Daniel Fu Keung, et al.. (2019). A Strength-Based Cognitive Behaviour Therapy Approach to Recovery. 1 indexed citations
15.
Yu, Rose, et al.. (2017). Tensor Regression Meets Gaussian Processes. International Conference on Artificial Intelligence and Statistics. 482–490. 8 indexed citations
16.
Li, Yaguang, Rose Yu, Cyrus Shahabi, & Yan Liu. (2017). Graph Convolutional Recurrent Neural Network: Data-Driven Traffic Forecasting.. arXiv (Cornell University). 44 indexed citations
17.
Wong, Paul, et al.. (2017). Efficacy of a Multicomponent Intervention with Animal-Assisted Therapy for Socially Withdrawn Youth in Hong Kong. Society and Animals. 27(5-6). 614–627. 22 indexed citations
18.
Varma, Paroma, Bryan He, Dan Iter, et al.. (2016). Socratic Learning: Correcting Misspecified Generative Models using Discriminative Models. arXiv (Cornell University). 3 indexed citations
19.
Yu, Rose & Yan Liu. (2016). Learning from Multiway Data: Simple and Efficient Tensor Regression. arXiv (Cornell University). 373–381. 14 indexed citations
20.
Yu, Rose, et al.. (2015). Accelerated Online Low Rank Tensor Learning for Multivariate Spatiotemporal Streams. International Conference on Machine Learning. 238–247. 37 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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