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My research is partially supported by National Science Foundation and Simons Foundation.
My google scholar page.
Manuscripts
Ouyang, W., Wu, R., Hao, N., and Zhang, H.H. (2024)
Dynamic Supervised Principal Component Analysis for Classification.
[arXiv]
Wang, Z., Tu, M., Liu, Z., Wang, K.K., Fang, Y., Hao, N., Zhang, H.H., Que, J., Sun, X., Yu, A., and Ding, H., (2024)
An Iterative Approach to Polish the Nanopore Sequencing Basecalling for Therapeutic RNA Quality Control.
[bioRxiv]
Journal Papers
Wang, Z., Liu, Z., Fang, Y., Zhang, H.H., Sun, X., Hao, N., Que, J., and Ding, H., (2024)
Training Data Diversity Enhances the Basecalling of Novel RNA Modification-Induced Nanopore Sequencing Readouts.
Nature Communications, to appear. [bioRxiv]; Code
Wang, 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]; Code
Zhao, 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 SCHACE
Hao, 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]
Wu, R. and Hao, N. (2022)
Quadratic Discriminant Analysis by Projection.
Journal of Multivariate Analysis, 190, 104987. [PDF] [arXiv]; R package QDAP
Hao, 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 SSSS
Shin, 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 bwd
Xiao, 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 modSaRa2
Hao, 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 RAMP
Niu, 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 SPCALDA
Hao, 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 codes
Xiao, 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 modSaRa
Niu, 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 codes
Hao, N. and Zhang, H.H. (2014).
Interaction Screening for Ultra-High Dimensional Data.
Journal of the American Statistical Association, 109, 1285-1301. [PDF]; Matlab codes
Hao, 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 SaRa
Fan, 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
Niu, Y.S., Hao, N. and An, L. (2011).
Detection of Rare Functional Variants Using Group ISIS.
BMC Proceedings, 5(Suppl 9):S108. [PDF]
Miscellaneous
Ph.D. dissertation: D-bar Spark Theory and Deligne Cohomology
Key words: Cheeger-Simons differential characters, Chern classes, Harvery-Lawson spark characters, hypercohomology, Massey product, Nadel’s conjecture, secondary geometric invariants.
The main results of my dissertation were uploaded in arXiv:
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