Jane-Ling Wang, Ph.D.

Education

• UC Berkeley, Ph.D.

Research Interests

• Deep Learning
• Conformal Inference
• Causal Inference
• Functional and Longitudinal Data Analysis
• Survival Analysis

Selected Works

• Y. Chen, S.-C. Lin, Y. Zhou, O. Carmichael, H.-G. Müller and J.-L. Wang. (2024). Gradient Synchronization for Multivariate Functional Data with Application to Brain Connectivity.   Journal of Royal Statistical Society, Series B: Statistical Methodology. 86,  694-713. (link)
• C. Zhu, J. Yao and J.-L. Wang (2024). Testing independence for sparse longitudinal data. Biometrika, 111 1187-1199. (link)   
• J. Hong, J. Yao, J. Mueller and J.-L. Wang (2024). SAND: Smooth imputation of sparse and noisy functional data with Transformer networks. Advances in Neural Information Processing Systems 37 (NeurIPS 2024) (link)
• Q. Zhong and J.-L. Wang (2024). Joint Modeling of Longitudinal and Survival Data. Annual Review of Statistics and Its Application, 12. 449-476. (link)
• C. Zhu and J.-L. Wang (2023). Testing Marginal Homogeneity for Functional Data: the Trouble with Sparse Data. Journal of Royal Statistical Society, Series B: Statistical Methodology.  85, 705-731. (link)
• Zhong, Q., Mueller, J. and Wang, J.-L. (2022). Deep Learning for the Partial Linear Cox Model. Annals of Statistics, 50, 1348-1375. (link)
• Lin, Z.  and Wang, J.-L. (2022).  Mean and covariance estimation for functional snippets. Journal of the American Statistical Association, 117, 348-360. (link)
• Lin, Z. Wang, J.-L. and Q. Zhong (2021). Basis expansions for functional snippets.  Biometrika, 108, 709-726. (link)
• Zhong, Q., Mueller, J. and Wang, J.-L. (2021). Deep Extended Hazard Models for Survival Analysis. Advances in Neural Information Processing Systems (NeurIPS). (link)
• Yao, J.,  Mueller, J. and Wang, J.-L. (2021).  Deep Learning for Functional Data Analysis with Adaptive Basis Layers. 2021 International Conference in Machine Learning (ICML). (link)
• Yang, P., Chen, J.,  Cho, C.-J., Wang, J.-L. and Jordan, M. (2020). Greedy Attack and Gumbel Attack: Generating Adversarial Examples on Discrete Data.  Journal of Machine Learning Research 21, 1-36. (link)
• Yang, P., Chen, J., Cho, C.-J., Wang, J.-L. and Jordan, M. (2020). ML-LOO: Detecting Adversarial Examples with Feature Attribution. Proceedings of the 34th AAAI Conference on Artificial Intelligence (AAAI). (link)