University of Alberta Department of Mathematical and Statistical Sciences
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Peer-reviewed * indicates trainees supervised; co-first authors are underlined

Refereed Journal Papers:

  1. Jiang, B., Raftery, A., Steele, R., and Wang, N. (2023). Privacy-Preserved and High-Utility Synthesis Strategy for Risk-Based Stratified Subgroups of Canadian Scleroderma Patient Registry Data, Annals of Applied Statistics, invited revision submitted
  2. Xie, J.*, Yan, X., Jiang, B., and Kong, L. (2023). Statistical inference for smoothed quantile regression with streaming data, Journal of Econometrics, invited revision submitted.
  3. Han, D., Xie, J.*, Liu, J., Sun, L., Huang, J., Jiang, B., and Kong, L. (2023). Inference on High-dimensional Single-index Models with Streaming Data,, Journal of Machine Learning Research, invited revision submitted.
  4. Xie, J.*, Shi, E.*, Shang, Z., Sang, P., Jiang, B., and Kong, L. (2023). Scalable inference in functional linear regression with streaming data, Annals of Applied Statistics, invited revision submitted.
  5. Liu, Y., Tu, W., Bao, Y., Jiang, B. and Kong, L. (2023). Asymmetric Estimation for Varying-coefficient Additive model with Functional Response in Reproducing Kernel Hilbert Space, Statistica Sinica, invited revision submitted.
  6. Li, J. , Kong, L., Jiang, B. and Tu, W. (2022). High-dimensional outlier detection and variable selection via adaptive weighted mean regression, Statistica Sinica, invited revision submitted.
  7. Yu, D.*, Pietrosanu, M.*, Tu, W., Mizera, I., Jiang, B., Kong, L. (2023). Functional Linear Partial Quantile Regression with Guaranteed Convergence for Neuroimaging Data Analysis. Statistics in Bioscience, accepted.
  8. Guo, W.*, Zhang, X., Jiang, B., Kong, L., and Hu, Y. (2022). Wavelet-based Bayesian approximate kernel method for high-dimensional data analysis, Computational Statistics, accepted.
  9. Xie, H.*, Pietrosanu, M.*, Liu, Y., Tu, W., Jiang, B., and Kong, L. (2023). Differentially Private Regularized Stochastic Convex Optimization with Heavy-Tailed Data, Statistica Sinica, accepted.
  10. Wang, Y.*, Jiang, B., Kong, L., and Zhang, Z. (2023). M-estimation on varying coefficient model with functional response in reproducing kernel Hilbert space, Bernoulli, accepted.
  11. Zhang, N., Liu, P.-, Kong, L., Jiang, B., and Huang, J-Z. (2023). Functional Linear Quantile Regression on a Two-dimensional Domain, Bernoulli, accepted.
  12. Xie, J.*, Ding, X., Jiang, B., Yan, X., and Kong, L., (2023). High dimensional model averaging for quantile regression, Canadian Journal of Statistics, accepted.
  13. Yan, X.*, Xie, J.*, Tu, W., Jiang, B., and Kong, L. (2023). Scalable Inference for Individual Treatment Effect, Statistics and Its Interface, accepted.
  14. Yu, P.*, Zhao, K., Jiang, B., Petkova, E., Tarpey, T., and Ogden, R. T. (2023). Associations Between EEG-Defined Subgroups and Antidepressant Response: A Joint Mixture of Probabilistic Multilinear Principal Component Analysis Modeling Approach, Statistics and Its Interface, accepted.
  15. Fan, C.*, Zhang, N.*, Jiang, B. and Liu, W. (2023). Using Deep Neural Networks Coupled with Principal Component Analysis for Ore Production Forecasting at Open-pit Mine Sites. Journal of Rock Mechanics and Geotechnical Engineering, doi: https://doi.org/10.1016/j.jrmge.2023.06.005.
  16. Fan, C.*, Zhang, N.*, Jiang, B. and Liu, W. (2023). Weighted Ensembles of Artificial Neural Networks based on Gaussian Mixture Modeling for Truck Productivity Prediction at Open-Pit Mines. Mining, Metallurgy & Exploration, 40, 583-598
  17. Mosquera, L., El Emam, K., Ding, L.*, Sharma, V., Zhang, X.H., Kababji, S.E., Carvalho, C., Hamilton, B., Palfrey, D., Kong, L. and Jiang, B., (2023). A method for generating synthetic longitudinal health data. BMC Medical Research Methodology, 23(1), pp.1-21.
  18. Fan, C.*, Zhang, N.*, Jiang, B. and Liu, W. (2022). Prediction of Truck Productivity at Mine Sites using Tree-based Ensemble Models Combined with Gaussian Mixture Modelling. International Journal of Mining, Reclamation and Environment, 37(1): 1-21
  19. Fan, C.*, Zhang, N.*, Jiang, B. and Liu, W. (2022). Preprocessing Large Datasets using Gaussian Mixture Modeling to Improve Prediction Accuracy of Truck Productivity at Mine Sites. Archives of Mining Sciences, 67 (2022), 4, 661-680.
  20. Tu, W., Jiang, B., and Kong, L. (2022). Discussion on Measuring Housing Vitality from Multi-Source Big Data and Machine Learning. Journal of American Statistical Association, 117 (539), 1060-1062
  21. Tu, W., Fu, F., Cobzas, D., Kong, L., Jiang, B. and Huang, C. (2022). Low Rank plus Sparse Decomposition of fMRI Data with Application to Alzheimer's Disease. Frontiers in Neurosciences, 16. https://doi.org/10.3389/fnins.2022.826316
  22. Jiang, Y.*, Mosquera, L., Jiang, B., Kong, L., and Emam, K. (2022). Measuring Re-identification Risk Using a Synthetic Estimator to Enable Data Sharing. PLoS ONE 17(6): e0269097.
  23. Pietrosanu, M.*, Shu, H., Jiang, B., Kong, L., Heo, G. He, Q., Gilmore, J., and Zhu, H. (2021). Estimation for bivariate quantile varying coefficient model with application in diffusion tensor imaging data analysis. Biostatistics, in press.
  24. Jiang, B., Raftery, A., Steele, R., and Wang, N. (2021). Balancing Inferential Integrity and Disclosure Risk via Model Targeted Masking and Multiple Imputation, Journal of American Statistical Association, in press.
  25. Hu, S., Alshehabi AI-Ani, J., Hughes, K., Denier, N., Konnikov, A., Ding, L.*, Xie, J.*, Hu, Y., Tarafdar, M., Jiang, B., Kong, L. and Dai, H. (2022). Balancing Gender Bias in Job Advertisements with Text-Level Bias Mitigation. Frontiers in Big Data, Vol 5. 805713.
  26. Pietrosanu, M.*, Kong, L., Yuan, Y., Bell, R., Letourneau, N. and Jiang, B. (2022). Associations between Longitudinal Gestational Weight Gain and Scalar Infant Birth Weight: A Bayesian Joint Modeling Approach. Entropy, 24, 232.
  27. Liu, M.*, Pietrasonu, M., Kong, L., Jiang, B. and Zhou, X. (2022). Reproducing Kernel based Partial Functional Expectile Regression. Canadian Journal of Statistics, 50(1), 241-266.
  28. Lee C.H., Jiang, B., Nakhaei-Nejad, M., Barilla, D., Blevins, G. and Giuliani, F. (2021). Cross-Sectional Analysis of Peripheral Blood Mononuclear Cells in Lymphopenic and Non-lymphopenic Relapsing Remitting Multiple Sclerosis Patients Treated with Dimethyl Fumarate. Multiple Sclerosis and Related Disorders, 52, 103003.
  29. Li, C., Tong, C.L.*, Niu, D., Jiang, B., Zuo, X., Cheng, L., Xiong, J., and Yang, J. (2021). Similarity Embedding Networks for Robust Human Activity Recognition. ACM Transactions on Knowledge Discovery from Data (TKDD), 15(6), 1-17.
  30. Pietrosanu, M.*, and Jiang, B. (2021). Discussion of Statistical Disease Mapping for Heterogeneous Neuroimaging Studies. Canadian Journal of Statistics, 49(1), 39-42.
  31. Pietrosanu, M.*, Gao J., Kong, L., Jiang, B., and Niu, D. (2021). Advanced algorithms for penalized quantile and composite quantile regression. Computational Statistics, 36(1), 333-346.
  32. Su, T., Wang, Y.*, Liu, Y., Branton, W., Asahchop, E., Power, C., Jiang, B., and Kong, L. (2020). Sparse Multicategory Generalized Distance Weighted Discrimination in Ultra-High Dimensions. Entropy, 22(11), 1257.
  33. Vekhande, C., Jiang, B., and Kate, M. (2020). Screening for Cognitive Impairment, Being Cognizant of the Liminal Deities and Demons. Canadian Journal of Neurological Sciences, 47(6), 731-733.
  34. Jiang, B., Petkova, E., Tarpey, T. and 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.
  35. Wang, Y.*, Kong, L., Jiang, B., Zhou, X., Yu, S., Zhang, L., Heo, G.(2019). Wavelet-based Lasso in Functional Linear Quantile Regression. Journal of Statistical Computation and Simulation, 89:6, 1111-1130.
  36. Zhang, L., Yu, D., Jiang, B., Mizera, I. and Kong, L. (2019). Sparse Wavelet Estimation in Quantile Regression with Multiple Functional Predictors. Computational Statistics & Data Analysis, 136: 12-29.
  37. Petkova, E., Tarpey, T., Ogden R. T., Ciarleglio, A., Jiang, B., et. al. (2017). Statistical analysis plan for stage 1 EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care) study. Contemporary Clinical Trials Communications, Vol. 6, 22-30.
  38. Jiang, B., Petkova, E., Tarpey, T. and Ogden R. T. (2017). Latent class modeling using matrix covariates with application to identifying early placebo responders based on EEG signals. The Annals of Applied Statistics, Vol. 11, No. 3, 1513-1536.
  39. Jiang, B., Elliott, M. R., Sammel, M. D. and Wang, N. (2016). Bayesian Model Assessments in Evaluating Mixtures of Longitudinal Trajectories and Their Associations with Cross-Sectional Health Outcomes. Statistics and Its Interface, Vol. 9, 183-201.
  40. Jiang, B., Sammel, M. D., Freeman, E. W. and Wang, N. (2015). Bayesian estimation of associations between identified longitudinal hormone subgroups and age at final menstrual period. BMC Medical Research Methodology, Vol. 15, 106.
  41. Jiang, B., Elliott, M. R., Sammel, M. D. and Wang, N. (2015). Joint Modeling of Cross- Sectional Health Outcomes and Longitudinal Predictors via Mixtures of Means and Variances. Biometrics, Vol. 71(2), 487-497.
  42. Jiang, B., Wang, N., Sammel, M. D. and 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. Vol. 64(5), 731-753.
  43. Jiang, B. and Carriere, K. C. (2014). Smoothing Age-Period-Cohort Models: A Generalized Additive Model Approach. Statistics in Medicine, Vol. 33(4), 595-606.
  44. Sadowski, D. C., Ackah, F., Jiang, B. and Svenson, L. W. (2010). Achalasia: Incidence, Prevalence and Survival. A Population Based Study. Nerogastroenterology and Motility, Vol. 22, 256-261.
  45. Cree, M., Lalji, M., Jiang, B. and Carriere, K. C. (2009). Under-reporting of Compensable Mesothelioma in Alberta. American Journal of Industrial Medicine, Vol. 52, 526-533.
  46. Cree, M., Lalji, M., Jiang, B., Carriere, K. C., Beach, J. and Kamruzzaman, A. (2009). Explaining Albertas Rising Mesothelioma Rates. Chronic Diseases in Canada, Vol. 29(4): 144-152.

Refereed Conference Proceedings:

  1. 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 (acceptance rate: 21.3%).
  2. 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 (acceptance rate: 21.3%).
  3. 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) (acceptance rate: 26.1%).
  4. Liu, P., Liu, Y., Zhu, R., Kong, L., Jiang, B. and Niu, D. (2023). Optimal Smooth Approximation for Quantile Matrix Factorization, SIAM International Conference on Data Mining (SDM) 2023 (acceptance rate: 27.4%).
  5. Feng, W., Li, X., Kong, L., Jiang, B. and Yan, X.* (2023). P-learning for Two-sided Markets, KDD23 Workshop on Decision Intelligence and Analytics for Online Marketplaces (selected as an oral spotlight)
  6. Liu, Y., Hu, Q., Ding, L.*, Jiang, B. and Kong, L. Online Local Differential Private Quantile Inference via Self-normalization, Proceedings of the 40th International Conference on Machine Learning (ICML) 2023 (acceptance rate: 27.9%).
  7. Chen, X., Diao, D.*, Chen, H., Yao, H., Piao, H., Sun, Z., Yang, Z., Goebel, R., Jiang, B., and Chang, Y. (2023). The Sufficiency of Off-policyness and Soft Clipping: PPO is still Insufficient according to an Off-policy Measure, Proceedings of the 37th AAAI Conference on Artificial Intelligence 2023 (acceptance rate: 19.6%).
  8. Liu, M.*, Ding, L.*, Yu, D., Liu, W., Kong, L. and Jiang, B. (2022). Conformalized Fairness via Quantile Regression, Proceeding of the 36th Conference on Neural Information Processing Systems (NeurIPS) (acceptance rate: 25.6%).
  9. Liu, Y., Sun, K., Jiang, B., and Kong, L. (2023). Identification, Amplification and Measurement: A Bridge to Gaussian Differential Privacy, Proceeding of the 36th Conference on Neural Information Processing Systems (NeurIPS) (acceptance rate: 25.6%).
  10. Ding, L.*, Yu, D.*, Xie, J.*, Guo, W.*, Hu, S., Liu, M.*, Kong, L. Dai, H., Bao, Y., Jiang, B. (2022) Word Embeddings via Causal Inference: Gender Bias Reducing and Semantic Information Preserving. Proceedings of the 2022 AAAI Conference on Artificial Intelligence, accepted.
  11. Wang, Y.*, Pan, B., Tu, W., Liu, P., Jiang, B., Gao, C., Lu, W. Jiu, S. and Kong, L. (2022). Sample Average Approximation for Dependent Data: Performance Guarantee and Tractability. Proceedings of the 2022 AAAI Conference on Artificial Intelligence, accepted.
  12. Sun, K., Wang, Y.*, Liu, Y., Zhao, Y., Pan, B., Jui, S., Jiang, B. and Kong, L. (2021). Damped Anderson Mixing for Deep Reinforcement Learning: Acceleration, Convergence, and Stabilization. Proceedings of NeurIPS 2021 Conference.
  13. Tu, W., Liu, P*., Liu, Y., Kong, L., Li, G., Jiang, B., Yao, H., and Jui, S. (2021). Nonsmooth Lowrank Matrix Recovery: Methodology,Theory and Algorithm. Proceedings of the Future Technologies Conference (FTC) 2021
  14. Li, C., Niu, D., Jiang, B., Zuo, X., and Yang, J. (2021). Meta-HAR: Federated Representation Learning for Human Activity Recognition. The Web Conference (WWW) 2021 (acceptance rate: 20.6%).
  15. Hu, Y., Liu, P.*, Ge, K., Kong, L., Jiang, B. and Niu, D. (2020). Learning Privately over Distributed Features: An ADMM Sharing Approach. NeurIPS-SpicyFL 2020.
  16. Tu, W., Liu, P.*, Zhao, J., Liu, Y., Kong, L., Li, G., Jiang, B., Tian, G., and Yao, H. (2019). M-estimation in Low-rank Matrix Factorization: a General Framework. 2019 IEEE 19th International Conference on Data Mining (ICDM), 568-577 (acceptance rate: 9.08%).
  17. Yao, H., Zhu, D., Jiang, B., Yu, P.* (2019). Negative Log Likelihood Ratio Loss for Deep Neural Network Classification, Proceedings of the Future Technologies Conference (FTC) 2019, 276-282.

Book Chapters:

  1. Konnikov, A., Rets, I., Hughes, K., Alshehabi Al-Ani, J., Denier, N., Ding, L.*, Hu, S., Hu, Y., Jiang, B., Kong, L., Tarafdar, M. and Yu, D.* (2022). Responsible AI for labour market equality (BIAS). In: How to Manage International Multidisciplinary Research Projects. Edward Elgar, Cheltenham. 75-87.
  2. Wang, Y.*, Lai, T., Jiang, B., Kong, L.and Zhang Z. (2022). Functional linear regression for partially observed functional data, Advances and Innovations in Statistics and Data Science, Editors: He, W., Wang, L., Chen, J. and Lin, C., Springer, 137-158.

PhD Thesis

  1. Jiang, B. (2014). Bayesian Joint Modeling of Longitudinal Trajectories and Health Outcome: A Broad Evaluation of Mean and Variation Features in Risk Profiles and Model Assessments. Ph.D. thesis, Advisor: Michael R. Elliott and Naisyin Wang, University of Michigan.

MSc Thesis

  1. Jiang, B. (2008). Estimation for Age-Period-Cohort Models: with Application to the Pleural Mesothelioma Data in Alberta 1980 to 2004. MSc thesis, Advisor: K. C. Carriere, University of Alberta.