Publication
My research is partially supported by the National Science Foundation and the Simons Foundation.
My Google Scholar page.
Manuscripts
Park, J., Hao, N., Niu, Y.S., and Hu, M. (2025)
Kernel Density Balancing.
[arXiv]Liao, S., Sun, X., Hao, N., and Zhang, H.H. (2025)
Interpretable Scalar-on-Image Linear Regression Models via the Generalized Dantzig Selector.
[arXiv]Liu, Z., Hao, N., Niu, Y.S., Xiao, H., and Ding, H. (2025)
Autocorrelation Test under Frequent Mean Shifts.
[arXiv]; R package SIP
Journal Papers
Li, X., Zhao, Y., Pan, Q., and Hao, N. (2025+)
Community Detection with Heterogeneous Block Covariance Model.
Journal of Computational and Graphical Statistics, to appear.
[PDF] [arXiv]; R package hbcmWang, Z., Tu, M., Liu, Z., Wang, K.K., Fang, Y., Hao, N., Zhang, H.H., Que, J., Sun, X., Yu, A., and Ding, H. (2025)
A Reference-guided Iterative Approach to Polish the Nanopore Sequencing Basecalling for Therapeutic RNA Quality Control.
Communications Biology, 8, 1406.
[PDF] [bioRxiv]Park, J., Zhao, Y., and Hao, N. (2025)
A Note on the Identifiability of Degree-Corrected Stochastic Block Model.
STAT, 14, e70067.
[PDF] [arXiv]Ouyang, W., Wu, R., Hao, N., and Zhang, H.H. (2025)
Dynamic Supervised Principal Component Analysis for Classification.
Journal of Computational and Graphical Statistics, 34, 1446–1455.
[PDF] [arXiv]; R package DSPCAWang, Z., Liu, Z., Fang, Y., Zhang, H.H., Sun, X., Hao, N., Que, J., and Ding, H. (2025)
Training Data Diversity Enhances the Basecalling of Novel RNA Modification-Induced Nanopore Sequencing Readouts.
Nature Communications, 16, 679.
[PDF] [bioRxiv]; CodeWang, Z., Fang, Y., Liu, Z., Hao, N., Zhang, H.H., Sun, X., Que, J., and Ding, H. (2024)
Adapting Nanopore Sequencing Basecalling Models for Modification Detection via Incremental Learning and Anomaly Detection.
Nature Communications, 15, 7148.
[PDF] [bioRxiv]; CodeZhao, Y., Hao, N., and Zhu, J. (2024)
Variational Estimators of the Degree-corrected Latent Block Model for Bipartite Networks.
Journal of Machine Learning Research, 25, 150, 1–42.
[PDF] [arXiv]Lu, Z., Hao, N., and Zhang, H.H. (2024)
Simultaneous Change-point Detection and Curve Estimation.
Statistics and Its Interface, 17, 493–500.
[PDF]; R package SCHACEHao, N., Niu, Y.S., and Xiao, H. (2023)
Equivariant Variance Estimation for Multiple Change-point Model.
Electronic Journal of Statistics, 17, 3811–3853.
[PDF] [arXiv]; R package EVEWu, R., and Hao, N. (2022)
Quadratic Discriminant Analysis by Projection.
Journal of Multivariate Analysis, 190, 104987.
[PDF] [arXiv]; R package QDAPHao, N., Niu, Y.S., Xiao, F., and Zhang, H. (2021)
A Super Scalable Algorithm for Short Segment Detection.
Statistics in Biosciences, 13, 18–33.
[PDF] [arXiv]; R package SSSSShin, S.J., Wu, Y., and Hao, N. (2020)
A Backward Procedure for Change-point Detection with Application to Copy Number Variation Detection.
The Canadian Journal of Statistics, 48, 366–385.
[PDF] [arXiv]; R package bwdXiao, F., Luo, X., Hao, N., Niu, Y.S., Xiao, X., Cai, G., Amos, C.I., and Zhang, H. (2019)
An Accurate and Powerful Method for Copy Number Variation Detection.
Bioinformatics, 35, 2891–2898.
[PDF]; R package modSaRa2Hao, N., Feng, Y., and Zhang, H.H. (2018)
Model Selection for High Dimensional Quadratic Regressions via Regularization.
Journal of the American Statistical Association, 113, 615–625.
[PDF] [arXiv]; R package RAMPNiu, Y.S., Hao, N., and Zhang, H.H. (2018)
Interaction Screening by Partial Correlation.
Statistics and Its Interface, 11, 317–325.
[PDF]Niu, Y.S., Hao, N., and Dong, B. (2018)
A New Reduced-Rank Linear Discriminant Analysis Method and Its Applications.
Statistica Sinica, 28, 189–202.
[PDF] [arXiv]; R package SPCALDAHao, N., and Zhang, H.H. (2017)
A Note on High Dimensional Linear Regression with Interactions.
The American Statistician, 71, 291–297.
[PDF] [arXiv]Hao, N., and Zhang, H.H. (2017)
Oracle P-values and Variable Screening.
Electronic Journal of Statistics, 11, 3251–3271.
[PDF]; R codesXiao, F., Niu, Y.S., Hao, N., Xu, Y., Jin, Z., and Zhang, H. (2017)
modSaRa: a computationally efficient R package for CNV identification.
Bioinformatics, btx212.
[PDF]; R package modSaRaNiu, Y.S., Hao, N., and Zhang, H. (2016)
Multiple Change-Point Detection, a Selective Overview.
Statistical Science, 31, 611–623.
[PDF] [arXiv]Hao, N., Dong, B., and Fan, J. (2015)
Sparsifying the Fisher Linear Discriminant by Rotation.
Journal of the Royal Statistical Society: Series B, 77, 827–851.
[PDF] [arXiv]; Matlab codesHao, N., and Zhang, H.H. (2014)
Interaction Screening for Ultra-High Dimensional Data.
Journal of the American Statistical Association, 109, 1285–1301.
[PDF]; Matlab codesHao, N., Niu, Y.S., and Zhang, H. (2013)
Multiple Change-Point Detection via a Screening and Ranking Algorithm.
Statistica Sinica, 23, 1553–1572.
[PDF]; R package SaRaFan, J., Guo, S., and Hao, N. (2012)
Variance Estimation Using Refitted Cross-Validation in Ultrahigh Dimensional Regression.
Journal of the Royal Statistical Society: Series B, 74, 37–65.
[PDF]
Conference Papers
Dong, B., and Hao, N. (2015)
Semi-supervised High Dimensional Clustering by Tight Wavelet Frames.
SPIE Optical Engineering + Applications.
[PDF]; Matlab codesNiu, Y.S., Hao, N., and An, L. (2011)
Detection of Rare Functional Variants Using Group ISIS.
BMC Proceedings, 5(Suppl 9): S108.
[PDF]
Miscellaneous
Hao, N., and Li, L. (2006)
Higher cohomology of the pluricanonical bundle is not deformation invariant.
[arXiv]Ph.D. dissertation: D-bar Spark Theory and Deligne Cohomology
Key words: Cheeger–Simons differential characters, Chern classes, Harvey–Lawson spark characters, hypercohomology, Massey product, Nadel’s conjecture, secondary geometric invariants.
The main results of my dissertation were uploaded in arXiv: