All Papers
Multimodal Learning
Data Integration
Spatial & Single-Cell Omics
Network
Signal Detection
High-Dimensional Statistics
Others
Some Preprints
Lu, Y., Enninful, A., Bao, S., Bai, Z., Xu, M. L., Xiao, Y.*, Fan, R.*, and Ma, Z.* (2025)SuperFocus enables whole-slide cell-level spatial omics from spot-based measurements and histology across modalities.
Cao, Y. and Ma, Z. (2025)MoDaH achieves rate optimal batch correction. [Data & Code]
Gui, Y., Ma, C., and Ma, Z. (2025)IndiSeek learns information-guided disentangled representations.
Li, Y.#, Zhang, Z.#, Enninful, A.#, Nam, J., Rajbhandari, P., Tian, H., Nam, J., Qin, X., Villazon, J., Fung, A. A., Jang, H., Bai, Z., Zhang, N. R., Stockwell, B. R., Fan, R.*, Xu, M. L.*, Ma, Z.*, and Shi, L.* (2024)All-optical multimodal mapping of single cell type-specific metabolic activities via REDCAT.
Nandy, S. and Ma, Z. (2024)Multimodal data integration and cross-modal querying via orchestrated approximate message passing. [Code]
Journal and Refereed Conference Publications
Enninful, A.#, Zhang, Z.#, Klymyshyn, D., Ingalls, M., Yang, M., Zong, H., Bai, Z., Farzad, N., Su, G., Baysoy, A., Nam, J., Lu, Y., Bao, S., Deng, S., Zhang, N. R., Braubach, O., Xu, M. L.*, Ma, Z.*, and Fan, R.* (2026)Integration of imaging-based and sequencing-based spatial omics mapping on the same tissue section via DBiTplus. Nature Methods .
Gui, Y., Ma, C., and Ma, Z. (2025)Multi-modal contrastive learning adapts to intrinsic dimensions of shared latent variables. NeurIPS .
Zhu, B.#, Gao, S.#, Chen, S.#, Wang, Y., Yeung, J., Bai, Y., Huang, A. Y., Yeo, Y. Y., Liao, G., Mao, S., Jiang, Z., Rodig, S. J., Wong, K.-C., Shalek, A. K., Nolan, G. P.*, Jiang, S.*, and Ma, Z.* (2025)CellLENS enables cross-domain information fusion for enhanced cell population delineation in single-cell spatial omics data. Nature Immunology , Vol.26, pp.963-974. CellLENS Python Package ]
Zhang, Z., Mathew, D., Lim, T., Mason, K., Martinez, C. M., Huang, S., Wherry, E. J., Susztak, K., Minn, A. J., Ma, Z.*, and Zhang, N. R.* (2025)Recovery of biological signals lost in single-cell batch integration with CellANOVA. Nature Biotechnology , Vol.43, pp.1861–1877. CellANOVA Python Package ]
Chen, S.#, Zhu, B.#, Huang, S., Hickey, J., Lin, K., Snyder, M., Greenleaf, W., Nolan, G. P.*, Zhang, N. R.*, and Ma, Z.* (2024)Integration of spatial and single-cell data across modalities with weakly linked features. Nature Biotechnology , Vol.42, pp.1096-1106. MaxFuse Python Package ] [Documentation ]
Hickey, J. W., et al. (2023)Organization of the human intestine at single cell resolution. Nature , Vol.619, pp.572-584.
Ma, Z. and Nandy, S. (2023)Community detection with contextual multilayer networks. IEEE Transactions on Information Theory , Vol.46(5), pp.3203-3239.
Gao, S. and Ma, Z. (2023)Sparse GCA and thresholded gradient descent. Journal of Machine Learning Research , Vol.24(135), pp.1−61.
Ma, Z. and Yang, F. (2023)Sample canonical correlation coefficients of high-dimensional random vectors with finite rank correlations. Bernoulli , Vol.29(3), 1905-1932.
Zhu, B.#, Chen, S.#, Bai, Y., Chen, H., Liao, G., Mukherjee, N., Vazquez, G., McIlwain, D. R., Tzankov, A., Lee, I. T., Matter, M. S., Goltsev, Y., Ma, Z.*, Nolan, G. P.*, and Jiang, S.* (2023)Robust single-cell matching and multi-modal analysis using shared and distinct features. Nature Methods , Vol.20, pp.304-315.MARIO Python Package ]
Gao, F., Ma, Z. and Yuan, H. (2022)Community detection in sparse latent space models. Journal of Machine Learning Research , Vol.23(322), pp.1-50.
Chen, S., Liu, S. and Ma, Z. (2022)Global and individualized community detection in inhomogeneous multilayer networks. Annals of Statistics , Vol.50(5), pp.2664-2693.
Banerjee, D. and Ma, Z. (2022)Optimal signal detection in some spiked random matrix models: Likelihood ratio tests and linear spectral statistics. Annals of Statistics , Vol.50(4), pp.1910-1932.
Chen, X., Li, X., and Ma, Z. (2022)Nonconvex matrix completion with linearly parameterized factors. Journal of Machine Learning Research , Vol.23(207), pp.1-35.
Gao, C. and Ma, Z. (2022)Testing equivalence of clustering. Annals of Statistics , Vol.50(1), pp.407-429.
Ding, J., Ma, Z., Wu, Y. and Xu, J. (2021)Efficient random graph matching via degree profiles. Probability Theory and Related Fields , Vol.179, pp.29-115.
Ma, Z., Ma, Z. and Yuan, H. (2020)Universal latent space model fitting for large networks with edge covariates. Journal of Machine Learning Research , Vol.21(4), pp.1-67.
Ma, Z., Ma, Z. and Sun, T. (2020)Adaptive estimation in two-way sparse reduced-rank regression. Statistica Sinica , Vol.30, pp.2179-2201.
Xia, C. H., Ma, Z., Cui, Z., Bzdok, D., Basset, D. S., Satterthwaite, T. D., Shinohara, R. T., Witten D. (2020)Multi-scale network regression for brain--phenotype associations. Human Brain Mapping , Vol.41(10), pp.2553-2566.
Gao, C., Ma, Z., Zhang, A. Y., and Zhou, H. H. (2018)Community detection in degree-corrected block models. Annals of Statistics , Vol.46(5), pp.2153–2185.
Xia, C. H., Ma, Z., Ciric, R., Gu, S., Betzel, R., Kaczkurkin, A., Calkins, M., Cook, P., de la Garza, A. G., Vandekar, S., Cui, Z., Moore, T., Roalf, D., Ruparel, K., Wolf, D., Davatzikos, C., Gur, R., Gur, R., Shinohara, R., Bassett, D., and Satterthwaite, T. (2018)Linked dimensions of psychopathology and connectivity in functional brain networks. Nature Communications , Vol.9, Article number:3003.
Gao, C., Ma, Z., and Zhou, H. H. (2017)Sparse CCA: Adaptive estimation and computational barriers. Annals of Statistics , Vol.45(5), pp.2074–2101. [Matlab Code]
Kennedy, E. H., Ma, Z., McHugh, M. D., and Small, D. S. (2017)Nonparametric methods for doubly robust estimation of continuous treatment effects. Journal of the Royal Statistical Society, Series B Vol.79(4), pp.1229-1245.
Gao, C., Ma, Z., Zhang, A. Y., and Zhou, H. H. (2017)Achieving optimal misclassification proportion in stochastic block models. Journal of Machine Learning Research , Vol.18(60), pp.1-45.
Gao, C., Lu, Y., Ma, Z., and Zhou, H. H. (2016)Optimal estimation and completion of matrices with biclustering structures. Journal of Machine Learning Research , Vol.17(161), pp.1-29.
Cai, T. T., Li, X., and Ma, Z. (2016)Optimal rates of convergence for noisy sparse phase retrieval via thresholded Wirtinger flow. Annals of Statistics , Vol.44(5), pp.2221-2251.
Yang, D., Ma, Z., and Buja, A. (2016)Rate optimal denoising of simultaneously sparse and low rank matrices. Journal of Machine Learning Research , Vol.17(92), pp.1-27.
Ma, Z. and Wu, Y. (2015)Volume ratio, sparsity, and minimaxity under unitarily invariant norms. IEEE Transactions on Information Theory , Vol.61(12), pp.6939-6956.
Gao, C., Ma, Z., Ren, Z., and Zhou, H. H. (2015)Minimax estimation in sparse canonical correlation analysis. Annals of Statistics , Vol.43(5), pp.2168-2197.
Ma, Z. and Wu, Y. (2015)Computational barriers in minimax submatrix detection. Annals of Statistics , Vol.43(3), pp.1089-1116.
Cai, T. T., Ma, Z., and Wu, Y. (2015)Optimal estimation and rank detection for sparse spiked covariance matrices. Probability Theory and Related Fields , Vol.161(3), pp.781-815.
Yang, D., Ma, Z., and Buja, A. (2014) A sparse SVD method for high-dimensional data. Journal of Computational and Graphical Statistics , Vol.23(4), pp.923-942.
Cai, T. T., Low, M., and Ma, Z. (2014)Adaptive confidence bands for nonparametric regression functions. Journal of the American Statistical Association , Vol.109, Issue 507, pp.1054-1070. [local version] [supplement]
Cai, T. T., Ma, Z., and Wu, Y. (2013)Sparse PCA: Optimal rates and adaptive estimation. Annals of Statistics , Vol.41, pp.3074-3110.
Ma, Z. (2013) Sparse principal component analysis and iterative thresholding. Annals of Statistics , Vol.41, pp.772-801. [complete version with supplement] [Matlab Code]
Cai, T. T. and Ma, Z. (2013) Optimal hypothesis testing for high-dimensional covariance matrices. Bernoulli , Vol.19, pp.2359-2388.
Johnstone, I. M. and Ma, Z. (2012)Fast approach to the Tracy-Widom law at the edge of GOE and GUE. Annals of Applied Probability , Vol.22, pp.1962-1988. [Matlab Code]
Ma, Z. (2012)Accuracy of the Tracy-Widom limits for the extreme eigenvalues in white Wishart matrices. Bernoulli , Vol.18, pp.322-359. [supplement] [R package]
Zhao, H., Ma, Z., Tibshirani, R., Higgins, J. P. T., Ljungberg, B., and Brooks, J. D. (2009)PLoS ONE 4(6): e6039.
Ma, Z., Xie, X., and Geng, Z. (2008)Structural learning of chain graphs via decomposition. Journal of Machine Learning Research , Vol.9, pp.2847-2880. [R package]
Xie, X., Ma, Z., and Geng, Z. (2008)Some association measures and their collapsibility. Statistica Sinica , Vol.18, pp.1165-1183.
Ma, Z., Xie, X., and Geng, Z. (2006)Collapsibility of distribution dependence. Journal of the Royal Statistical Society, Series B , Vol.68, pp.127-133.
Invited Discussions and Reviews
Gao, C. and Ma, Z. (2021)Minimax rates in network analysis: Graphon estimation, community detection and hypothesis testing. Statistical Science , Vol.36(1), pp.16-33.
Gao, C. and Ma, Z. (2020)Discussion of `Network cross-validation by edge sampling'. Biometrika , Vol.107(2), pp.281-284.
Low, M. and Ma, Z. (2015)Annals of Statistics , Vol.43(4), pp.1448-1454.
Archived Preprints
Chen, S., Jiang, S., Ma, Z., Nolan, G. P., and Zhu, B. (2022)One-way matching of datasets with low rank signals.
Banerjee, D. and Ma, Z. (2017)Optimal hypothesis testing for stochastic block models with growing degrees.