Academic Background

Research Interest

Selected Publications

1. Xu, Q., Yuan, Y., Wang, J. and Qu, A. (2023+). Crowdsourcing utilizing subgroup structure of latent factor modeling. Journal of the American Statistical Association, in press.
2. Zhen, Y. and Wang, J. (2023+). Community detection in general hypergraph via graph embedding. Journal of the American Statistical Association, in press.
3. Zhang, J. , He, X. and Wang, J. (2022). Directed community detection with network embedding. Journal of the American Statistical Association, 117, 1809-1819.
4. Zhao, R., He, X. and Wang, J. (2022). Learning linear non-Gaussian directed acyclic graph with diverging number of nodes. Journal of Machine Learning Research, 23(269):1-34.
5. Feng, L. and Wang, J. (2022). Projected robust PCA with application to smooth image recovery. Journal of Machine Learning Research, 23(249):1-41.
6. Dai, B. , Shen, X. and Wang, J. (2022). Embedding learning. Journal of the American Statistical Association, 117, 307-319.
7. Dai, B. , Shen, X. , Wang, J. and Qu, A. (2021). Scalable collaborative ranking for personalized prediction. Journal of the American Statistical Association, 116, 1215 – 1223.
8. Dai, B. , Wang, J. , Shen, X. and Qu, A. (2019). Smooth neighborhood recommender systems. Journal of Machine Learning Research, 20(16): 1 – 24.
9. Bi, X. , Qu, A. , Wang, J. and Shen, X. (2017). A group-specific collaborative recommender. Journal of the American Statistical Association, 112, 1344 – 1353.
10. Wang, J. , Shen, X. , Sun, Y. and Qu, A. (2017). Automatic summarization by existing and novel tags. Biometrika, 104, 273 – 290.
11. Wang, J. , Shen, X. , Sun, Y. and Qu, A. (2016). Classification with unstructured predictors and an application to sentiment analysis. Journal of the American Statistical Association, 111, 1242 – 1253.
12. Yang, L. , Lv, S. and Wang, J. (2016). Model-free variable selection in reproducing kernel Hilbert space. Journal of Machine Learning Research, 17(78): 1 – 24.
13. Sun, W. , Wang, J. and Fang, Y. (2013). Consistent selection of tuning parameters in high-dimensional penalized regression. Journal of Machine Learning Research, 14, 3419 – 3440.
14. Wang, J. (2010). Consistent selection of the number of clusters via cross validation. Biometrika, 97, 893 – 904.
15. Wang, J. , Shen, X. and Pan, W. (2009). On large margin hierarchical classification with multiple paths. Journal of the American Statistical Association, 104, 1213 – 1223.
16. Wang, J. , Shen, X. and Pan, W. (2008). On efficient large margin semisupervised learning: methodology and theory. Journal of Machine Learning Research, 10, 719 – 742.
17. Wang, J. , Shen, X. and Liu, Y. (2008). Probability estimation for large margin classifiers. Biometrika, 95, 149 – 167.
18. Wang, J. and Shen, X. (2007). Large margin semi-supervised learning. Journal of Machine Learning Research, 8, 1867 – 1891.

Major Research Grants

1. RGC GRF-11311022 “A statistical framework for structure-preserving embedding of signed networks”, PI, 2023-2025
2. RGC GRF-11301521 “Joint modeling of hypergraph networks for community detection and graph embedding”, PI, 2022-2024
3. RGC GRF-11304520 “Hierarchical modeling of directed acyclic graphs: estimation, selection and asymptotics”, PI, 2021-2023
4. RGC GRF-11300919 “Latent factor modeling of large-scale directed networks with covariates and structures”, PI, 2020-2022
5. RGC GRF-11303918 “Scalable kernel-based variable selection with theoretical guarantee”, PI, 2019-2021
6. RGC GRF-11331016 “Large-scale multi-label classification and its application to unstructured text data”, PI, 2017-2019
7. RGC GRF-11302615 “Model-free variable selection via learning gradients”, PI, 2016-2018

Professional Services

• Panel Member, HK RGC Physical Sciences Panel (JRS), since 2022
• Associate Editor, Journal of the American Statistical Association – T&M, since 2023
• Associate Editor, Statistica Sinica, since 2020
• Associate Editor, Annals of Institute of Statistical Mathematics, since 2018
• Associate Editor, Statistics and Its Interface, since 2017