Research

Funding Acknowledgements

NSF grants DMS-2311399, DMS-1848579, and DMS-1513653.

Preprints

• Chen, H.
  Cursive: The trace from the curse of dimensionality.
  arXiv:2609.08610

• Liu, M., Chen, H.
  Graph-based change-point detection for partially observed high-dimensional data.
  arXiv:2609.06550

• Chen, H.
  What FID hides: Detecting, ranking, and diagnosing deviations in generative evaluation.
  arXiv:2608.24881

• Sun, M., Chen, H.
  KAP-CPD: Kernel aggregation for change-point detection in dynamic networks.
  arXiv:2605.14463

• Liu, M., Zhou, D., Chen, H.
  Generalized independence test.
  arXiv:2409.07745

• Lin, X., Chen, H.
  UBSea: A unified community detection framework.
  arXiv:2310.04934

• Zhu, Y., Chen, H.
  Mitigating dimensionality effects with robust graph constructions for testing.
  arXiv:2307.15205

• Chen, H., Lin, X.
  High-dimensional clustering via nearest-neighbor asymmetry.
  arXiv:2305.00578

• Song, H., Chen, H.
  New graph-based multi-sample tests for high-dimensional and non-Euclidean data.
  arXiv:2205.13787

• Zhang, Y., Chen, H.
  Graph-based multiple change-point detection.
  arXiv:2110.01170

• Chen, H., Chen, S., Deng, X.
  A universal nonparametric event detection framework for Neuropixels data.
  bioRxiv:650671

• Chen, H.
  Change-point detection for multivariate and non-Euclidean data with local dependency.
  arXiv:1903.01598.

Publications

• Mo, X., Chen, H. (2026)
  Rank-transformed dissimilarity profiles for high-dimensional classification.
  NeurIPS 2026, accepted.
  

• Song, H., Chen, H. (2026)
  A fast and effective kernel two-sample test for large-scale data.
  Statistics and Computing, DOI:10.1007/s11222-026-10969-8.
  

• Zhang, J., Chen, H., Zhou, X. (2026)
  A new non-parametric test for multivariate paired data from pair matching or paired designs.
  Journal of Statistical Planning and Inference, DOI:10.1016/j.jspi.2026.106443.
  

• Zhou, D., Chen, H. (2025)
  Asymptotic distribution-free change-point detection for modern data based on a new ranking scheme.
  IEEE Transactions on Information Theory, 71(8):6183-6197.

• Chu, L., Chen, H. (2025)
  On the tightness of graph-based statistics.
  Electronic Journal of Statistics, 19(1):1425-1452.

• Song, H., Chen, H. (2024)
  Practical and powerful kernel-based change-point detection.
  IEEE Transactions on Signal Processing, 72:5174-5186. R package: kerSeg

• Song, H., Chen, H. (2024)
  Generalized kernel two-sample tests.
  Biometrika, 111(3):755-770. R package: kerTests
  

• Zhu, Y., Chen, H. (2024)
  Limiting distributions of graph-based test statistics on sparse and dense graphs. [PDF]
  Bernoulli, 30(1):770-796.

• Zhou, D., Chen, H. (2023)
  A new ranking scheme for modern data and its application to two-sample hypothesis testing.
  Proceedings of Thirty Sixth Conference on Learning Theory, PMLR 195:3615-3668

• Hu, S., Huang, J., Chen, H., Chan, H. (2023)
  Likelihood scores for sparse signal and change-point detection.
  IEEE Transactions on Information Theory, 69(6):4065-4080.

• Chen, H., Chu, L. (2023)
  Graph-based change-point analysis.
  Annual Review of Statistics and Its Application, 10:475-499.

• Chen, H., Xia, Y. (2023).
  A normality test for high-dimensional data based on a nearest neighbor approach.
  JASA, Theory and Methods, 118(541):719-731.

• Chu, L., Chen, H. (2022)
  Sequential change-point detection for high-dimensional and non-Euclidean data.
  IEEE Transactions on Signal Processing, 70:4498-4511. [Corrections] R package: gStream

• Song, H., Chen, H. (2022)
  Asymptotic distribution-free change-point detection for data with repeated observations.
  Biometrika, 109(3):783-798. R package: gSeg
  

• Liu, Y., Chen, H. (2022)
  A fast and efficient change-point detection framework based on approximate k-nearest neighbor graphs.
  IEEE Transactions on Signal Processing, 70:1976-1986.
  

• Chen, H., Small, D. (2022).
  New multivariate tests for assessing covariate balance in matched observational studies.
  Biometrics, 78:202–213. R package: BalanceCheck

• Zhang, J., Chen, H. (2022).
  Graph-based two-sample tests for data with repeated observations.
  Statistica Sinica, 32:391-415. R package: gTests

• Fu, P., Schoenball, M., Ajo-Franklin, J., Chai, C., Maceira, M., Morris, J., Wu, H., Knox, H., Schwering, P., White, M., Burghardt, J., Strickland, C., Johnson, T., Vermeul, V., Sprinkle, P., Roberts, B., Ulrich, C., Guglielmi, Y., Cook, P., Dobson, P., Wood, T., Frash, L., Huang, L., Ingraham, M., Pope, J., Smith, M., Neupane, G., Doe, T., Roggenthen. W., Horne, R., Singh, A., Zoback, M., Wang, H., Condon, K., Ghassemi, A., Chen, H., McClure, M., Vandine, G., Blankenship, D., Kneafsey, T., EGS Collab Team. (2021).
  Close observation of hydraulic fracturing at EGS collab experiment 1: fracture trajectory, microseismic interpretations, and the role of natural fractures.
  Journal of Geophysical Research - Solid Earth, 126(7), e2020JB020840.

• Zhang, Q., Mahdi, G., Tinker, J., Chen, H. (2020).
  A graph-based multi-sample test for identifying pathways associated with cancer progression.
  Computational Biology and Chemistry, 87:107285.
  

• Chen, H. (2019).
  Sequential change-point detection based on nearest neighbors.
  The Annals of Statistics, 47(3):1381-1407. R package: gStream

• Chu, L., Chen, H. (2019).
  Asymptotic distribution-free change-point detection for multivariate and non-Euclidean data.
  The Annals of Statistics, 47(1):382-414. R package: gSeg

• Chen, H., Chen, X. and Su, Y. (2018).
  A weighted edge-count two sample test for multivariate and object data.
  JASA, Theory and Methods, 113(523):1146-1155. R package: gTests

• Urrutia, E., Chen, H., Zhou, Z., Zhang, N. and Jiang, Y. (2018).
  Integrative pipeline for profiling DNA copy number and inferring tumor phylogeny.
  Bioinformatics, bty057. R pipeline: Marathon

• Maxwell, K., Wubbenhorst, B., Wenz, B., Sloover, D., Pluta, J., Emery, L., Barrett, A., Kraya, A., Anastopoulos, I., Yu, S., Jiang, Y., Chen, H., Zhang, N., Hackman, N., D'Andrea, K., Daber, R., Morrissette, J., Mitra, N., Feldman, M., Domchek, S. and Nathanson, K. L. (2017).
  BRCA locus-specific loss of heterozygosity in germline BRCA1 and BRCA2 carriers.
  Nature Communications, 8:319.

• Chen, H., Jiang, Y., Maxwell, K., Nathanson, K. and Zhang, N. (2017).
  Allele-specific copy number estimation by whole Exome sequencing.
  The Annals of Applied Statistics, 11(2), 1169. R package: falconx

• Wang, X., Chen, H., Zhang, N. (2017).
  DNA copy number profiling using single cell sequencing.
  Briefings in Bioinformatics, bbx004. R pipeline: SCNV

• Chen, H. and Friedman, J. H. (2017).
  A new graph-based two-sample test for multivariate and object data.
  JASA, Theory and Methods, 112(517):397-409. R package: gTests

• Wang, X., Chen, M., Yu, X., Pornputtapong, N., Chen, H., Zhang, N. R., Powers, S. and Krauthammer, M. (2016).
  Global copy number profiling of cancer genomes.
  Bioinformatics, 32(6):926-928.

• Chen, H. and Zhang, N. R. (2015).
  Graph-based change-point detection.
  The Annals of Statistics, 43(1):139-176. R package: gSeg

• Chen, H., Bell, J. M., Zavala, N. A., Ji, H. P. and Zhang, N. R. (2014).
  Allele-specific copy number profiling by next-generation DNA sequencing.
  Nucleic Acids Research, 43(4):e23. R package: falcon

• Nadauld, L. D., Garcia, S., Natsoulis, G., Bell, J. M., Miotke, L., Hopmans, E. S., Xu, H., Pai, R. K., Palm, C., Regan, J. F., Chen, H., Flaherty, P., Ootani, A., Zhang, N. R., Ford, J. M., Kuo, C. J. and Ji, H. P. (2014).
  Metastatic tumor evolution and organoid modeling implicate TGFBR2 as a cancer driver in diffuse gastric cancer.
  Genome Biology 15:428.

• Chen, H. and Zhang, N. R. (2013).
  Graph-based tests for two-sample comparisons of categorical data.
  Statistica Sinica 23:1479-1503. R package: gCat

• Chen, H., Xing, H. and Zhang, N. R. (2011).
  Estimation of parent specific DNA copy number in tumors using high-density genotyping arrays.
  PLoS Computational Biology 7(1):e1001060

Note: In the author list, underlined are students under my supervision.