Bei Jiang
About
Appointed Canada CIFAR AI Chair – 2022
Bei Jiang’s research aims to advance the field of health data analysis by developing efficient analytics tools, enabling secure data sharing and developing fair machine learning algorithms. One significant challenge in health data analysis is dealing with data complexity and heterogeneity, for example, electronic health records and neuroimaging data. Jiang’s research focuses on developing efficient computational tools that can effectively handle these complex data types.
Additionally, the ability to share health data is crucial for advancing medical research and improving patient outcomes, but it must be done in a way that respects patient privacy. To that end, Jiang has been developing novel privacy tools that enable secure data sharing while protecting sensitive patient information.
Most recently, Jiang is investigating methods to identify and mitigate algorithmic biases. This is especially important in healthcare, as it can impact treatment decisions and lead to disparities in healthcare outcomes.
Awards
- Research Fellow, Statistical and Applied Mathematical Sciences Institute (SAMSI), 2015
Relevant Publications
- Zhao, S., Cui, W., Jiang, B., Kong, L., and Yan, X. (2024). Optimal Smooth Approximation for Quantile Matrix Factorization, Proceedings of the 38th AAAI Conference on Artificial Intelligence 2024
- Jiang, Y., Liu, Y., Yan, X., Charest, A-S., Kong, L., Jiang, B. (2024). Analysis of Differentially Private Synthetic Data: A Measurement Error Approach, Proceedings of the 38th AAAI Conference on Artificial Intelligence 2024
- Jiang, Y., Chang, X., Liu, Y., Ding, L., Kong, L., and Jiang, B. (2023). Gaussian Differential Privacy on Riemannian Manifolds, Proceeding of the 37th Conference on Neural Information Processing Systems (NeurIPS)
- Liu, M., Ding, L., Yu, D., Liu, W., Kong, L., & Jiang, B. (2023) Conformalized Fairness via Quantile Regression. In Advances in Neural Information Processing Systems.
- Liu, Y., Sun, K., Jiang, B., & Kong, L. (2023) Identification, Amplification and Measurement: A bridge to Gaussian Differential Privacy. In Advances in Neural Information Processing Systems.