Bei Jiang
About
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
- 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.
- Jiang, B., Raftery, A. E., Steele, R. J., & Wang, N. (2021). Balancing Inferential Integrity and Disclosure Risk Via Model Targeted Masking and Multiple Imputation. Journal of the American Statistical Association, 117(537), 52-66.
- Jiang, B., Petkova, E., Tarpey, T., & Ogden, R. T. (2020). A Bayesian approach to joint modeling of matrix‐valued imaging data and treatment outcome with applications to depression studies. Biometrics, 76(1), 87-97.
- Jiang, B., Wang, N., Sammel, M. D., & Elliott, M. R. (2015). Modeling Short-and long-term characteristics of follicle stimulating hormone as predictors of severe hot flashes in Penn ovarian aging study. Journal of the Royal Statistical Society. Series C, Applied statistics, 64(5), 731.
Support Us
CIFAR is a registered charitable organization supported by the governments of Canada, Alberta and Quebec, as well as foundations, individuals, corporations and Canadian and international partner organizations.