Academic & Professional Qualification
- Ph.D. in Statistics (The Pennsylvania State University), 2021;
- Bachelor in Statistics (Renmin University of China), 2016.
Zhanrui Cai is an assistant professor in the area of Innovation and Information Management at the HKU Business School. Previously, he was an assistant professor in the Department of Statistics, Iowa State University.
- High dimensional inference
- Distribution-free inference
- Conformal inference
- Causal discovery
- Differential privacy
- Awan, J., and Cai, Z. (2023) “One Step to Efficient Synthetic Data”, Statistica Sinica, forthcoming.
- Xia, X., and Cai, Z. (2023) “Adaptive False Discovery Rate Control with Privacy Guarantee”, Journal of Machine Learning Research, 24(252): 1-35.
- Cai, Z., Lei, J., Roeder, K. (2023) “Asymptotic distribution-free independence test for high dimension data”, Journal of the American Statistical Association.
- Cai, Z., Lei, J., Roeder, K. (2022) “Model-free prediction test with application to genomics data”, Proceedings of the National Academy of Sciences.
- Du, J., Cai, Z., Roeder, K. (2022) Robust probabilistic modeling for single-cell multimodal mosaic integration and imputation via scVAEIT, Proceedings of the National Academy of Sciences
- Cai, Z., Li, C., Wen, J., Yang, S. (2022) Asset splitting algorithm for ultrahigh dimensional portfolio selection and its theoretical property, Journal of Econometrics.
- Cai, Z., Zhang, Y., Li, R. (2022) “A distribution-free conditional independence test with application to causal discovery”, Journal of Machine Learning Research.
- Cai, Z., Xi, D., Zhu, X., Li, R. (2022) Causal discoveries for high dimensional mixed data, Statistics in Medicine.
- Tong, Z., Cai, Z., Yang, S., Li, R. (2022) Model-free conditional feature screening with FDR control, Journal of the American Statistical Association.
- Zhu, X., Cai, Z., Ma, Y. (2021) Network functional autoregression model, Journal of the American Statistical Association.
- Cai, Z., Li, R., Zhu, L. (2020) “Online sufficient dimension reduction through sliced inverse regression”, Journal of Machine Learning Research.