[64] Wang, L., Ni, Y., and Gaynanova, I. (2026) “Truncated Gaussian Copula Principal Component Analysis with Application to Pediatric Acute Lymphoblastic Leukemia Patients’ Gut Microbiome.” Statistical Methods in Medical Research (just accepted).

[63] [Editor’s Choice Article] Coulter, A., Tong, C., Ni, Y., and Jiang, Y. (2025) “distQTL: Distribution Quantitative Trait Loci Identification by Population-Scale Single-Cell Data.” NAR Genomics and Bioinformatics, 7(4), lqaf155. 

[62] Choi, J., Chung, H. C., Gaynanova, I., and Ni, Y. (2025) “Bayesian Segmented Gaussian Copula Factor Model for Single-Cell Sequencing Data.” Bayesian Analysis (just accepted).

[61] Chung, H.C., Ni, Y., and Gaynanova, I. (2025) “Sparse Semiparametric Discriminant Analysis for High-Dimensional Zero-Inflated Data.” Journal of Machine Learning Research (just accepted).

[60] Franklin, S., Ramont, C., Batool, M., McMahon, S., Sahasrabhojane, P., Blazier, J., Kontoyiannis, D., Ni, Y., and Galloway-Peña, J. (2025) “Characterization of Antibiotic Administration Factors Associated with Microbiome Disruption and Subsequent Antibiotic-Resistant Infection and Colonization Events in Acute Myeloid Leukemia Patients Receiving Chemotherapy.” Antibiotics (just accepted).

[59] Zhou, F., He, K., and Ni, Y. (2025) “Tree-Based Additive Noise Models for Nonlinear Causal Discovery with Interactions.” Biometrics (just accepted).

[58] Turner, D., and Ni, Y. (2025) “A Bayesian Nonparametric Method for Jointly Clustering Multiple Spatial Transcriptomic Datasets and Simultaneous Gene Selection.” Scientific Reports (just accepted).

[57] Chakrabarti, A., Ni, Y., Pati, D., and Mallick, B. (2025) “Global-Local Dirichlet Processes for Identifying Pan-Cancer Subpopulations Using Both Shared and Cancer-Specific Data.” Annals of Applied Statistics (just accepted).

[56] Chakrabarti, A., Ni, Y., Pati, D., and Mallick, B. (2025) “Global-Local Dirichlet Processes for Clustering Grouped Data in the Presence of Group-Specific Idiosyncratic Variables.” Proceedings of the 42nd International Conference on Machine Learning (ICML), PMLR (just accepted).

[55] [ASA Student Paper Award] Choi, J., Chapkin, R., and Ni, Y. (2025) “Bayesian Differential Causal Directed Acyclic Graphs for Observational Zero-Inflated Counts with an Application to Two-Sample Single-Cell Data.” Annals of Applied Statistics (just accepted). 

[54] Arthur, K., Smallman, R., Engler, S., Lowe, J., Ni, Y., and Fields, S. (2025) “Associations Among Mask Wearing Behavior and the Theory of Planned Behavior Constructs in Undergraduate Students During and Post-Mask Mandate.” Health Psychology Open (just accepted).

[53] Sarkar, B., and Ni, Y. (2025) “MR.RGM: An R Package for Fitting Bayesian Multivariate Bidirectional Mendelian Randomization Networks.” Bioinformatics (just accepted).

[52] Jin, W., Ni, Y., Spence, A., Rubin, L., and Xu, Y. (2025) “Directed Cyclic Graphs for Simultaneous Discovery of Time-Lagged and Instantaneous Causality from Longitudinal Data Using Instrumental Variables.” Journal of Machine Learning Research (just accepted).

[51] Chen, L., Acharyya, S., Luo, C., Ni, Y., and Baladandayuthapani, V. (2025) “Probabilistic Graphical Modeling under Heterogeneity.” Cell Reports Methods (just accepted).

[50] Dallakyan, A., and Ni, Y. (2025) “Generalized Criterion for Identifiability of Additive Noise Models Using Majorization.” In International Conference on Artificial Intelligence and Statistics (AISTATS) (just accepted).

[49] Yao, T., Ni, Y., Bhadra, A., Kang, J., and Baladandayuthapani, V. (2025) “Robust Bayesian Graphical Regression Models for Assessing Tumor Heterogeneity in Proteomic Networks.” Biometrics (just accepted).

[48] Ni, Y., Chen, S., and Wang, Z. (2025) “Causal Structural Modeling of Survey Questionnaires via a Bootstrapped Ordinal Bayesian Network Approach.” Psychometrika, 90(1), 229–250.

[47] Fiani, D., Engler, S., Ni, Y., Fields, S., and Calarge, C. (2024) “Iron Deficiency and Internalizing Symptoms Among Adolescents in the National Health and Nutrition Examination Survey.” Nutrients, 16(21), 3643.

[46] [ASA Student Paper Award] Chakrabarti, A., Ni, Y., Morris, E.R.A., Salinas, M.L., Chapkin, R.S., and Mallick, B. (2024) “Graphical Dirichlet Process for Clustering Non-Exchangeable Grouped Data.” Journal of Machine Learning Research, 25(323), 1–56.

[45] Das, S., Niu, Y., Ni, Y., Mallick, B., and Pati, D. (2024) “Blocked Gibbs Sampler for Hierarchical Dirichlet Processes.” Journal of Computational and Graphical Statistics (just accepted).

[44] Chakrabarti, A., Ni, Y., and Mallick, B. (2024) “JOBS: JOint BayeSian Modeling of Cell Dependence and Gene Associations in Spatially Resolved Transcriptomic Data.” Scientific Reports, 14(1), 9516.

[43] Whitfield-Cargile, C.M., Chung, H.C., Coleman, M.C., Cohen, N.D., Chamoun-Emanuelli, A.M., Ivanov, I., Goldsby, J.R., Davidson, L.A., Gaynanova, I., Ni, Y., and Chapkin, R.S. (2024) “Integrated Analysis of Gut Metabolome, Microbiome, and Exfoliome Data in an Equine Model of Intestinal Injury.” Microbiome, 12, 74.

[42] Jin, W., Ni, Y., Spence, A., Rubin, L., and Xu, Y. (2024) “A Bayesian Approach for Investigating the Pharmacogenetics of Combination Antiretroviral Therapy in People with HIV.” Biostatistics, 25(4), 1034–1048.

[41] Wang, Z., Zhou, F., He, K., and Ni, Y. (2024) “Multi-Way Overlapping Clustering by Bayesian Tensor Decomposition.” Statistics and Its Interface, 17, 219–230.

[40] [Reproducibility Award] Niu, Y., Ni, Y., Pati, D., and Mallick, B. (2024) “Covariate-Assisted Bayesian Graph Learning for Heterogeneous Data.” Journal of the American Statistical Association, 119(547), 1985–1999.

[39] Rogovchenko, V., Sibu, A., and Ni, Y. (2024) “Scalar-Function Causal Discovery for Generating Causal Hypotheses with Observational Wearable Device Data.” Pacific Symposium on Biocomputing, 29.

[38] [NeurIPS Scholar Award] Roy, S., Wong, R., and Ni, Y. (2023) “Directed Cyclic Graph for Causal Discovery from Multivariate Functional Data.” Advances in Neural Information Processing Systems (NeurIPS), 36.

[37] Zhou, F., He, K., Wang, K., Xu, Y., and Ni, Y. (2023) “Functional Bayesian Networks for Discovering Causality from Multivariate Functional Data.” Biometrics, 79, 3279–3293.

[36] Chen, S., He, K., He, S., Ni, Y., and Wong, R. (2023) “Bayesian Nonlinear Tensor Regression with Functional Fused Elastic Net Prior.” Technometrics, 65(4), 524–536. 

[35] [Media Coverage by Johns Hopkins University] Jin, W., Ni, Y., O’Halloran, J., Spence, A., Rubin, L., and Xu, Y. (2023) “A Bayesian Decision Framework for Optimizing Sequential Combination Antiretroviral Therapy in People with HIV.” Annals of Applied Statistics, 17(4), 3035–3055.

[34] Choi, J., and Ni, Y. (2023) “Model-Based Causal Discovery for Zero-Inflated Count Data.” Journal of Machine Learning Research, 24(200), 1–32.

[33] Zhou, F., He, K., and Ni, Y. (2023) “Individualized Causal Discovery with Latent Trajectory Embedded Bayesian Networks.” Biometrics, 79(4), 3191–3202.

[32] Kidd, B., Wang, K., Xu, Y., and Ni, Y. (2023) “Federated Learning for Sparse Bayesian Models with Applications to Electronic Health Records and Genomics.” Pacific Symposium on Biocomputing, 28.

[31] Ni, Y. (2022) “Bivariate Causal Discovery for Categorical Data via Classification with Optimal Label Permutation.” Advances in Neural Information Processing Systems (NeurIPS), 35.

[30] Ni, Y., Stingo, F.C., and Baladandayuthapani, V. (2022) “Bayesian Covariate-Dependent Gaussian Graphical Models with Varying Structure.” Journal of Machine Learning Research, 23(242), 1–29.

[29] Das, P., Peterson, C., Ni, Y., Reuben, A., Zhang, J., Zhang, J., Do, K.A., and Baladandayuthapani, V. (2022) “Bayesian Hierarchical Quantile Regression for Precision Immuno-Oncology.” Biometrics, 79(3), 2474–2488.

[28] Zhou, F., He, K., Cai, J., Davidson, L., Chapkin, R., and Ni, Y. (2022) “A Unified Bayesian Framework for Bi-Overlapping-Clustering Multi-Omics Data via Sparse Matrix Factorization.” Statistics in Biosciences, 15(3), 669–691.

[27] Ni, Y., and Mallick, B. (2022) “Ordinal Causal Discovery.” Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR, 180:1530–1540.

[26] Zhou, F., He, K., and Ni, Y. (2022) “Causal Discovery with Heterogeneous Observational Data.” Proceedings of the 38th Conference on Uncertainty in Artificial Intelligence (UAI), PMLR, 180:2383–2393.

[25] Chung, H.C., Gaynanova, I., and Ni, Y. (2022) “Phylogenetically Informed Bayesian Truncated Copula Graphical Models for Microbial Association Networks.” Annals of Applied Statistics, 16(4), 2437–2457.

[24] Li, Y., Ni, Y., Rubin, L., Spence, A., and Xu, Y. (2022) “BAGEL: A Bayesian Graphical Model for Inferring Drug Effect Longitudinally on Depression in People with HIV.” Annals of Applied Statistics, 16(1), 21–39.

[23] [ASA Student Paper Award] Jin, W., Ni, Y., Rubin, L., Spence, A., and Xu, Y. (2022) “A Bayesian Nonparametric Approach for Inferring Drug Combination Effects on Mental Health in People with HIV.” Biometrics, 78, 988–1000. 

[22] Ni, Y., Baladandayuthapani, V., Vannucci, M., and Stingo, F.C. (2022) “Bayesian Graphical Models for Modern Biological Applications.” Statistical Methods and Applications (with Discussion), 31(2), 197–225.

[21] Wang, Z., Ni, Y., Jing, B., Wang, D., Zhang, H., and Xing, E.P. (2022) “DNB: A Joint Learning Framework for Deep Bayesian Nonparametric Clustering.” IEEE Transactions on Neural Networks and Learning Systems, 1–11.

[20] [ASA Student Paper Award] Zhou, F., He, K., Li, Q., Chapkin, R., and Ni, Y. (2021) “Bayesian Biclustering for Metagenomic Sequencing Data via Multinomial Matrix Factorization.” Biostatistics, 23(3), 891–909. 

[19] [Spotlight Presentation (acceptance rate 4%)] Choi, J., Chapkin, R., and Ni, Y. (2020) “Bayesian Causal Structural Learning with Zero-Inflated Poisson Bayesian Networks.” Advances in Neural Information Processing Systems (NeurIPS), 33, 5887–5897.

[18] Ni, Y., Jones, D., and Wang, Z. (2020) “Consensus Variational and Monte Carlo Algorithms for Bayesian Nonparametric Clustering.” IEEE International Conference on Big Data, 204–209.

[17] Ni, Y., Ji, Y., and Müller, P. (2020) “Consensus Monte Carlo for Random Subsets using Shared Anchors.” Journal of Computational and Graphical Statistics, 29(4), 703–714.

[16] Wang, Z., Jing, B., Ni, Y., Dong, N., Xie, P., and Xing, E.P. (2020) “Relationship-Aware Multi-Class Adversarial Domain Adaptation.” European Conference on Artificial Intelligence, 24th.

[15] Vickman, R.E., Broman, M.M., Lanman, N.A., Franco, O.E., Sudyanti, P.A.G., Ni, Y., Ji, Y., Helfand, B.T., Petkewicz, J., Paterakos, M.C., Crawford, S.E., Ratliff, T.L., and Hayward, S.W. (2020) “Heterogeneity of Human Prostate Carcinoma-Associated Fibroblasts Implicates a Role for Subpopulations in Myeloid Cell Recruitment.” Prostate, 80(2), 173–185.

[14] Ni, Y., Müller, P., and Ji, Y. (2020) “Bayesian Double Feature Allocation for Phenotyping with Electronic Health Records.” Journal of the American Statistical Association, 115(532), 1620–1634.

[13] Ni, Y., Müller, P., Diesendruck, M., Williamson, S., Zhu, Y., and Ji, Y. (2020) “Scalable Bayesian Nonparametric Clustering and Classification.” Journal of Computational and Graphical Statistics, 29(1), 53–65.

[12] [Editors’ Highlights] Ge, T., Chen, C.Y., Ni, Y., Feng, Y.C.A., and Smoller, J.W. (2019) “Polygenic Prediction via Bayesian Regression and Continuous Shrinkage Priors.” Nature Communications, 10(1), 1776.

[11] Ni, Y., Müller, P., Shpak, M., and Ji, Y. (2019) “Parallel-Tempered Feature Allocation for Large-Scale Tumor Heterogeneity with Deep Sequencing Data.” In Pharmaceutical Statistics: MBSW 2016. Springer Proceedings in Mathematics & Statistics, vol 218.

[10] Ni, Y., Stingo, F.C., Ha, M.J., Akbani, R., and Baladandayuthapani, V. (2019) “Bayesian Hierarchical Varying-Sparsity Model with Application to Cancer Proteo-Genomics.” Journal of the American Statistical Association, 114(525), 48–60.

[9] Ni, Y., Stingo, F.C., and Baladandayuthapani, V. (2019) “Bayesian Graphical Regression.” Journal of the American Statistical Association, 114(525), 184–197.

[8] Ni, Y., Ji, Y., and Müller, P. (2018) “Reciprocal Graphical Models for Integrative Gene Regulatory Network Analysis.” Bayesian Analysis, 13(4), 1095–1110.

[7] Ni, Y., Müller, P., Zhu, Y., and Ji, Y. (2018) “Heterogeneous Reciprocal Graphical Models.” Biometrics, 74(2), 606–615.

[6] Ni, Y., Müller, P., Lin, W., and Ji, Y. (2018) “Bayesian Graphical Models for Computational Network Biology.” BMC Bioinformatics, 19(3), 63.

[5] Shpak, M., Ni, Y., Lu, J., and Müller, P. (2017) “Variance in Estimated Pairwise Genetic Distance Under High versus Low Coverage Sequencing: The Contribution of Linkage Disequilibrium.” Theoretical Population Biology, 117, 51–63.

[4] Ni, Y., Stingo, F.C., and Baladandayuthapani, V. (2017) “Sparse Multi-Dimensional Graphical Models: A Unified Bayesian Framework.” Journal of the American Statistical Association, 112(518), 779–793.

[3] Ni, Y., Stingo, F.C., and Baladandayuthapani, V. (2015) “Bayesian Nonlinear Model Selection for Gene Regulatory Networks.” Biometrics, 71(3), 585–595.

[2] Guo, W., Ni, Y., and Ji, Y. (2015) “TEAMS: Toxicity- and Efficacy-Based Dose Insertion Design with Adaptive Model Selection for Phase I/II Dose-Escalation Trials in Oncology.” Statistics in Biosciences, 7(2), 432–459.

[1] Ni, Y., Stingo, F.C., and Baladandayuthapani, V. (2014) “Integrative Bayesian Network Analysis of Genomic Data.” Cancer Informatics, 13(s2), 39–48.