Selected Publications

Fang, C., He, H., Long, Q., Su, W.: Exploring Deep Neural Networks via Layer-Peeled Model: Minority Collapse in Imbalanced Training. Proceedings of the National Academy of Sciences (PNAS) 118(43): e2103091118, 2021.

Zhang Y., Long, Q. : Assessing Fairness in the Presence of Missing Data. 2021 Conference on Neural Information Processing Systems (NeurIPS 2021) 34: 16007-16019, 2021.

Chang C, Jang A, Manatunga A, Taylor A.T., Long, Q : A Bayesian Latent Class Model to Predict Kidney Obstruction Based on Renography and Expert Ratings in the Absence of Gold Standard. Journal of the American Statistical Association 115(532): 1645- 1663, 2020.

Wu, Y., Keoliya, M., Chen, K., Velingker, N., Li, Z., Getzen, E., Long, Q., Naik, M., Parikh, R. and Wong, E. : DISCRET: Synthesizing Faithful Explanations For Treatment Effect Estimation. The Forty-First International Conference on Machine Learning (ICML 2024), Spotlight (3.5% acceptance rate). 2024.

Zhou, Z., Ataee Tarzanagh, D., Hou, B., Tong, B., Xu, J., Feng, Y., Long, Q., Shen, L.: Fair Canonical Correlation Analysis. 2023 Conference on Neural Information Processing Systems (NeurIPS 2023) 2023.

Getzen, E.J., Ungar, L., Mowery, D., Jiang, X., and Long, Q.: Mining for Equitable Health: Assessing the Impact of Missing Data in Electronic Health Records. Journal of Biomedical Informatics 139: 104269, 2023.

Chang, C., Deng, Y., Jiang, X., Long, Q.: Multiple Imputation for Analysis of Incomplete Data in Distributed Health Data Networks. Nature Communications 11(1): 5467, 2020.

Bu, Z., Dong, J., Long, Q., Su, W.: Deep Learning with Gaussian Differential Privacy. Harvard Data Science Review 2(3): 1-48, 2020.

Zheng, Q., Dong, J., Long, Q., Su, W.: Sharp Composition Bounds for Gaussian Differential Privacy via Edgeworth Expansion. Proceedings of the 37th International Conference on Machine Learning (ICML 2020) 119: 11420-11435, 2020.

Zhao, Y., Chang, C., and Long, Q.: Knowledge-guided statistical learning methods for analysis of high-dimensional -omics data in precision oncology. JCO Precision Oncology 3: 1-9, 2019.

Min EJ, Safo SE, Long Q: Penalized co-inertia analysis with applications to -omics data. Bioinformatics 35(6): 1018-1025, 2019 Notes: doi: 10.1093/bioinformatics/bty726.

Li Z, Roberts K, Jiang X, Long Q: Distributed Learning from Multiple EHR Databases: Contextual Embedding Models for Medical Events. Journal of Biomedical Informatics 92: 103138, 2019 Notes: doi: 10.1016/j.jbi.2019.103138. Epub 2019 Feb 27.

Zhao, Y.*, Chung, M., Johnson, B.A., Moreno, C.S., and Long, Q.: Hierarchical feature selection incorporating known and novel biological information: Identifying genomic features related to prostate cancer recurrence. Journal of the American Statistical Association 111(516): 1427-1439, 2016 Notes: *An earlier version won Yize Zhao the David P. Byar Travel Award from American Statistical Association’s Biometrics Section 2014.

Chang C, Kundu S, Long Q: Scalable Bayesian variable selection for structured high-dimensional data. Biometrics 74(4): 1372-1382, 2018 Notes: doi: 10.1111/biom.12882. Epub 2018 May 8.

Safo, S.E., Li, S., and Long, Q.: Integrative analysis of transcriptomic and metabolomic data via sparse canonical correlation analysis with incorporation of biological information. Biometrics 74(1): 300-312, 2018.

Long, Q., Xu, J., Osunkoya, A.O., Sannigrahi, S., Johnson, B.A., Zhou, W., Gillespie, T., Park, J.Y., Nam, R.K., Sugar, L., Stanimirovic, A., Seth, A.K., Petros, J.A., and Moreno, C.S.: Global transcriptome analysis of formalin-fixed prostate cancer specimens identifies biomarkers of disease recurrence. Cancer Research 74(12): 3228-3237, 2014.

Long, Q., Little, R.J., and Lin, X.: Causal inference in hybrid intervention trials involving treatment choice. Journal of the American Statistical Association 103(482): 474-484, 2008.