Preprints / Submitted:
Formentini, S. E., Liang, W., & Zhu, R. Confidence Band Estimation for Random Survival Forests [arXiv]
Yu, L., Zhu, L. Zhu, R., & Zhu, X. Corrected kernel principal component analysis for model structural change detection. [arXiv]
Ye, H., Zhou, W., Zhu, R., & Qu, A. Stage-Aware Learning for Dynamic Treatments [OpenReivew]
Zhou, W., Li, Y., Zhu, R., & Qu, A. Distributional Shift-Aware Off-Policy Interval Estimation: A Unified Error Quantification Framework [arXiv] [Wenzhuo won ICSA 2023 Junior Researcher Award]
Method / Theory:
Qiu, R., Yu, Z., & Zhu, R. (2024) Random Forests Weighted Local Fréchet Regression with Theoretical Guarantee. Journal of Machine Learning Research 25(107), 1-69. [arXiv]
Xu, T., Zhu, R., & Shao, X. (2024) On Variance Estimation of Random Forests with Infinite-Order U-statistics. Electronic Journal of Statistics, 18(1), 2135-2207. [arXiv]
Li, Y., Zhou, W., & Zhu, R. Quasi-optimal Reinforcement Learning with Continuous Treatments. ICLR 2023 [arXiv]
Chen, Y., Xu, T., Hakkani-Tur, D., Jin, D., Yang, Y., & Zhu, R. (2022). Calibrate and Debias Layer-wise Sampling for Graph Convolutional Networks. TMLR [arXiv]
Zhou, W., Zhu, R., & Qu, A. (2024) Solving Infinite Horizon Dynamic Treatment Regimes: A Proximal Temporal Consistency Learning Approach. Journal of the American Statistical Association. 119(545), 625-638. [arXiv] [Wenzhuo won NESS Student Research Awards 2022]
Cui, Y., Kosorok, M. R., Sverdrup, E., Wager, S., & Zhu, R. (2023) Estimating heterogeneous treatment effects with right-censored data via causal survival forests. Journal of the Royal Statistical Society: Series B, 85(2): 179-211. [arXiv].
Loyal, J., Zhu, R., Cui, Y., & Xin, Z. (2022) Dimension Reduction Forests: Local Variable Importance using Structured Random Forests. Journal of Computational and Graphical Statistics. 31(4): 1104-1113. [GitHub drforest] [arXiv]
Li, K., Yao, S., Zhang, Z., Cao, B., Wilson, C.M., Kalos, D., Kuan, P.F., Zhu, R. & Wang, X. (2022) Efficient gradient boosting for prognostic biomarker discovery. Bioinformatics, 38(6), 1631-1638.
Li, Y., Zhu, R., Yeh, M., & Qu, A. (2022) Dermoscopic Image Classification with Neural Style Transfer. Journal of Computational and Graphical Statistics. 31(4), 1318-1331. [arXiv]
Guo, B., Holscher, H. D., Auvil, L. S., Welge, M. E., Bushell, C. B., Novotny, J. A., Baer, D. J., Burd, N. A., Khan, N. A., & Zhu, R. (2021) Estimating Heterogeneous Treatment Effect on Multivariate Responses using Random Forests. Statistics in Biosciences. [GitHub: MOTE.RF]
Li, Y., Zhu, R., Qu, A., Ye, H. & Sun Z. (2021) Topic Modeling on Triage Notes With Semiorthogonal Nonnegative Matrix Factorization. Journal of the American Statistical Association, A&C 116(536): 1609-1624. [arXiv]
Cui Y., Zhu, R., Zhou, M. & Kosorok, M. R. (In press) Consistency of survival forest and tree models: splitting bias and correction. Statistica Sinica, Forthcoming. [arXiv] [Yifan Cui won Hannan Graduate Student Travel Award]
Zhou W., Zhu, R. & Zeng, D. (2021) A Parsimonious Personalized Dose Finding Model via Dimension Reduction. Biometrika, 108, no. 3 (2021): 643-659. [arXiv] [GitHub: orthoDr]
Zhao Y.Q., Zhu, R., Chen, G. & Zheng Y. (2020) Constructing Dynamic Treatment Regimes with Shared Parameters for Censored Data. Statistics In Medicine, 39 (9), 1250-1263. [arXiv].
Feng, Z., Lin, L., Zhu, R., & Zhu, L. (2019). Nonparametric variable selection and its application to additive models. Annals of the Institute of Statistical Mathematics, 1-28.
Mi, X., Zhu, R., Zou, F. (2019) Bagging and Deep Learning in Optimal Individualized Treatment Rules. Biometrics, 75(2):674-684. [R packages: dnnet and ITRlearn] [Example R File]
Sun, Q., Zhu, R., Wang, T. & Zeng, D. (2019)Counting Process Based Dimension Reduction Method for Censored Outcomes. Biometrika, 06(1):181-196. [arXiv] [R package: orthoDr]
Cui, Y., Zhu, R., Kosorok, M. R. (2017) Tree based weighted learning for estimating individualized treatment rules with censored data. Electronic Journal of Statistics, 11(2), 3927-3953. [arXiv]
Zhu, R., Zhao, Y., Chen, G., Ma, S., & Zhao H. (2017) Greedy Outcome Weighted Tree Learning of Optimal Personalized Treatment Rules. Biometrics. 73(2), 391–400.
Zhu, R., Zhao, Q., Zhao, H., & Ma, S. (2016) Integrating Multidimensional Omics Data for Cancer Outcome. Biostatistics. 17(4), 605-18.
Zhu, R., Zeng, D., & Kosorok, M. R. (2015) Reinforcement Learning Trees. Journal of the American Statistical Association. 110(512), 1770-1784. [R package RLT]
Zhu, R., Zhao, H., & Ma, S. (2014). Identifying Gene-Environment and Gene-Gene Interactions Using a Progressive Penalization Approach. Genetic Epidemiology, 38(4), 353-368.
Zhu, R., & Kosorok, M. R. (2012). Recursively imputed survival trees. Journal of the American Statistical Association, 107(497), 331-340.
Zhu, L., Zhu, R., & Song, S. (2008). Diagnostic checking for multivariate regression models. Journal of Multivariate Analysis 99 (9), 1841-1859.
Collaborative Research:
Maino Vieytes, C. A., Zhu, R., Gany, F., Burton-Obanla, A., & Arthur, A. E. (2022). Empirical Dietary Patterns Associated with Food Insecurity in US Cancer Survivors: NHANES 1999–2018. International journal of environmental research and public health, 19(21), 14062.
Di, S., Petch, J., Gerstein, H. C., Zhu, R., & Sherifali, D. (2022). Optimizing Health Coaching for Patients With Type 2 Diabetes Using Machine Learning: Model Development and Validation Study. JMIR formative research, 6(9), e37838.
Zhou, H., Zhu, R., Ung, A., & Schatz, B.. (In press) Population analysis of mortality risk: Predictive models from passive monitors using motion sensors for 100,000 UK Biobank participants. PLOS Digital Health. [Press Release]
Shinn, L. M., Mansharamani, A., Baer, D. J., Novotny, J. A., Charron, C. S., Khan, N. A., Zhu, R. & Holscher, H. D. (2022). Fecal Metabolites as Biomarkers for Predicting Food Intake By Healthy Adults. The Journal of Nutrition, 152 (12), 2956-2965.
D’Agnillo, F., Walters, K.A., Xiao, Y., Sheng, Z.M., Scherler, K., Park, J., Gygli, S., Rosas, L.A., Sadtler, K., Kalish, H., Blatti III, C.A., Zhu, R., ..., Kash, J. C. & Taubenberger, J. K., (2021). Lung epithelial and endothelial damage, loss of tissue repair, inhibition of fibrinolysis, and cellular senescence in fatal COVID-19. Science Translational Medicine, 13(620), p.eabj7790. [Press Release]
Robison, H.M., Chapman, C.A., Zhou, H., Erskine, C.L., Theel, E., Peikert, T., Lindestam Arlehamn, C.S., Sette, A., Bushell, C., Welge, M. and Zhu, R., Bailey, R. C., & Escalante, P., (2021). Risk assessment of latent tuberculosis infection through a multiplexed cytokine biosensor assay and machine learning feature selection. Scientific reports, 11(1), pp.1-10.
Taneja, I., Damhorst, G. L., Lopez‐Espina, C., Zhao, S. D., Zhu, R., Khan, S., ... & Bashir, R. (2021). Diagnostic and prognostic capabilities of a biomarker and EMR‐based machine learning algorithm for sepsis. Clinical and Translational Science.
Shinn, L., Li, Y., Mansharamani, A., Auvil, L. S., Welge, M. E., Bushell, C., Khan, N. A., Charron, C. S., Novotny, J. A., Baer, D. J., Zhu, R., Holscher, H. D., (2020) Fecal bacteria as biomarkers for predicting food intake in healthy adults. The Journal of Nutrition 151, no. 2 (2021): 423-433. [Newsletter] [Editor's Commentory]
Miao, R., Badger, T.C., Groesch, K., Diaz-Sylvester, P.L., Wilson, T., Ghareeb, A., Martin, J.A., Cregger, M., Welge, M., Bushell, C., Auvil, L., Zhu, R., Brard L. Braundmeier-Fleming, A. Laganà, ed., A. 2020. Assessment of peritoneal microbial features and tumor marker levels as potential diagnostic tools for ovarian cancer. PloS one 15, no. 1 (2020): e0227707.
Walters K-A, Zhu R, Welge M, Scherler K, Park J-K, Rahil, Z., Wang, H., Auvil L, Bushell C, Lee MY, Baxter D, Bristol T, Rosas LA, CervantesMedina A, Czajkowski L, Han A, Memoli MJ, Taubenberger JK, Kash JC. 2019. Differential effects of influenza virus NA, HA head, and HA stalk antibodies on peripheral blood leukocyte gene expression during human infection. Mbio 10, no. 3 (2019): e00760-19.
Yi, M., Zhu, R. & Stephens, R.M., 2018. GradientScanSurv—An exhaustive association test method for gene expression data with censored survival outcome. PloS one, 13(12), p.e0207590.
Hassan, U., Zhu, R. & Bashir, R., 2018. Multivariate computational analysis of biosensor's data for improved CD64 quantification for sepsis diagnosis. Lab on a Chip, 18(8), pp.1231-1240.
Taneja, I., Bobby Reddy Jr, B., Damhorst, G., Zhao, D., Hassan, U., Price, Z., Jensen, T., Ghonge, T., Patel, M., Wachspress, S., Winter, J., Rappleye, M., Smith, G., Healey, R., Ajmal, M., Anwaruddin, S., Khan, M., Patel, J., Rawal, H., Sarwar, R., Soni, S., Davis, B., Kumar, J., White, K., Bashir, R., & Zhu, R., "Combining Biomarkers with EMR Data to Improve Sepsis Identification." Scientific Reports 7, no. 1 (2017): 10800. [Newsletter]
La, E. H., Lich, K. H., Wells, R., Ellis, A. R., Swartz, M. S., Zhu, R., Morrissey, J. P. (2015) "Increasing Access to State Psychiatric Hospital Beds: Exploring Supply-side Solutions." Psychiatric Services, 67(5), 523-528.
La, E. H., Zhu, R., Lich, K. H., Ellis, A. R., Swartz, M. S., Kosorok, M. R., & Morrissey, J. P. (2014). The Effects of State Psychiatric Hospital Waitlist Policies on Length of Stay and Time to Readmission. Administration and Policy in Mental Health and Mental Health Services Research, 42(3), 332-42. [link]
Book Chapters:
Formentini, S. E., Cui, Y., & Zhu, R. (2022+) Random Forests for Survival Analysis and High-Dimensional Data. Springer Handbook of Engineering Statistics, 2nd ed. Editor: Hoang Pham. Springer London
Zhou, W., Li, Y., & Zhu, R. (2022+) Optimal Policy Learning for Individualized Treatment on Infinite Time Horizon. Precision Medicine: Methods and Applications. Editor: Yichuan Zhao. Springer London