Research

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.
  biorxiv:10.64898/2025.12.26.696575.
  


• Cao, Y. and Ma, Z. (2025)
  MoDaH achieves rate optimal batch correction.
  arXiv:2512.09259. [Data & Code]
  


• Gui, Y., Ma, C., and Ma, Z. (2025)
  IndiSeek learns information-guided disentangled representations.
  arXiv:2509.21584.
  


• 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.
  biorxiv:10.1101/2024.11.07.622511.
  


• Nandy, S. and Ma, Z. (2024)
  Multimodal data integration and cross-modal querying via orchestrated approximate message passing.
  arXiv:2407.19030. [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)
  Alteration of gene expression signatures of cortical differentiation and wound response in lethal clear cell renal cell carcinomas.
  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)
  Discussion: Frequentist coverage of adaptive nonparametric Bayesian credible sets.
  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.
  arXiv:2204.13858.
  


• Banerjee, D. and Ma, Z. (2017)
  Optimal hypothesis testing for stochastic block models with growing degrees.
  arXiv:1705.05305.