Research

Some Preprints

• Zhang, Z., Mathew, D., Lim, T., Huang, S., Wherry, E. J., Minn, A. J., Ma, Z., and Zhang, N. R. (2023)
  Signal recovery in single cell batch integration.
  biorxiv:10.1101/2023.05.05.539614. [Python Package]
  [ZM & NRZ are co-correspondence authors]


• 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.
  

Journal Publications

• Chen, S., Zhu, B., Huang, S., Hickey, J., Lin, K., Snyder, M., Greenleaf, W., Nolan, G. P., Zhang, N. R., and Ma, Z. (2023)
  Integration of spatial and single-cell data across modalities with weakly linked features.
  Nat Biotechnol. [Python Package] [Documentation]
  [SC & BZ are co-first authors, GPN, NRZ & ZM are co-correspondence authors]


• 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 Trans Info Theory, Vol.46(5), pp.3203-3239.
  


• Gao, S. and Ma, Z. (2023)
  Sparse GCA and thresholded gradient descent.
  J Mach Learn Res, 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.
  Nat Methods. [Python Package]
  [BZ & SC are co-first authors, ZM, GPN & SJ are co-correspondence authors]


• Gao, F., Ma, Z. and Yuan, H. (2022)
  Community detection in sparse latent space models.
  J Mach Learn Res, Vol.23(322), pp.1-50.
  


• Chen, S., Liu, S. and Ma, Z. (2022)
  Global and individualized community detection in inhomogeneous multilayer networks.
  Ann Statist, 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.
  Ann Statist, Vol.50(4), pp.1910-1932.
  


• Chen, X., Li, X., and Ma, Z. (2022)
  Nonconvex matrix completion with linearly parameterized factors.
  J Mach Learn Res, Vol.23(207), pp.1-35.
  


• Gao, C. and Ma, Z. (2022)
  Testing equivalence of clustering.
  Ann Statist, Vol.50(1), pp.407-429.
  


• Ding, J., Ma, Z., Wu, Y. and Xu, J. (2021)
  Efficient random graph matching via degree profiles.
  Probab Theory Relat Field, Vol.179, pp.29-115.
  


• Ma, Z., Ma, Z. and Yuan, H. (2020)
  Universal latent space model fitting for large networks with edge covariates.
  J Mach Learn Res, Vol.21(4), pp.1-67.
  


• Ma, Z., Ma, Z. and Sun, T. (2020)
  Adaptive estimation in two-way sparse reduced-rank regression.
  Stat 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.
  Hum Brain Mapp, 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.
  Ann Statist, 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.
  Nat Commun, Vol.9, Article number:3003.
  


• Gao, C., Ma, Z., and Zhou, H. H. (2017)
  Sparse CCA: Adaptive estimation and computational barriers.
  Ann Statist, 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.
  J Roy Stat Soc 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.
  J Mach Learn Res, 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.
  J Mach Learn Res, 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.
  Ann Statist, Vol.44(5), pp.2221-2251.
  


• Yang, D., Ma, Z., and Buja, A. (2016)
  Rate optimal denoising of simultaneously sparse and low rank matrices.
  J Mach Learn Res, Vol.17(92), pp.1-27.
  


• Ma, Z. and Wu, Y. (2015)
  Volume ratio, sparsity, and minimaxity under unitarily invariant norms.
  IEEE Trans Info 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.
  Ann Statist, Vol.43(5), pp.2168-2197.
  


• Ma, Z. and Wu, Y. (2015)
  Computational barriers in minimax submatrix detection.
  Ann Statist, 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.
  Probab Theory Relat Field, Vol.161(3), pp.781-815.
  


• Yang, D., Ma, Z., and Buja, A. (2014)
  A sparse SVD method for high-dimensional data.
  J Comput Graph Stat, Vol.23(4), pp.923-942.
  


• Cai, T. T., Low, M., and Ma, Z. (2014)
  Adaptive confidence bands for nonparametric regression functions.
  J Amer Statist Assoc, 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.
  Ann Statist, Vol.41, pp.3074-3110.
  


• Ma, Z. (2013)
  Sparse principal component analysis and iterative thresholding.
  Ann Statist, 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.
  Ann Appl Probab, 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.
  J Mach Learn Res, Vol.9, pp.2847-2880. [R package]
  


• Xie, X., Ma, Z., and Geng, Z. (2008)
  Some association measures and their collapsibility.
  Stat Sinica, Vol.18, pp.1165-1183.
  


• Ma, Z., Xie, X., and Geng, Z. (2006)
  Collapsibility of distribution dependence.
  J Roy Stat Soc 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.
  Statist Sci, 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.
  Ann Statist, Vol.43(4), pp.1448-1454.