Peer Reviewed — Statistics

  1. Lu, R., Zhu, H. and Wu, X. (2023) Estimating mutation rates in a Markov branching process using approximate Bayesian computation, Journal of Theoretical Biology,May 21;565:111467. ([main paper], [bibtex])

  2. Huo, S., Morris, J. S. and Zhu, H. (2023) Ultra-fast approximate inference using variational functional mixed models, Journal of Computational and Graphical Statistics,32:2,353-365. ([main paper], [supp. materials and code] , [bibtex])

  3. Wu, X. and Zhu, H. (2022) Association testing for binary trees—a Markov branching process approach, Statistics in Medicine, in press. ([link], [bibtex])

  4. Zhu, H., Versace, F., Cincirpini, P. M., Rausch, P. and Morris, J. S. (2018) Robust and Gaussian spatial functional regression models for analysis of event-related potentials, NeuroImage, 181 501-512. ([main paper], [supp. material], [code: compiled version], [bibtex])

  5. Zhu, H., Caspers, P., Morris, J. S., Wu, X. and Mueller, R. (2018) A unified analysis of structured sonar-terrain data using Bayesian functional mixed models, Technometrics, 60(1) 112-123. ([main paper], [supp. material], [bibtex])

  6. Zhu, H., Morris, J. S., Wei, F. and Cox, D. D. (2017) Multivariate functional response regression, with application to fluorescence spectroscopy in a cervical pre-cancer study, Computational Statistics and Data Analysis, 60(1) 112-123. ([main paper], [supp. material], [code], [bibtex])

  7. Zhu, H.*, Lu*, R., Ming, C., Gupta, A. K. and Mueller, R. (2017) Estimating parameters in complex systems with functional outputs---a wavelet-based approximate Bayesian computation approach, New Advances in Statistics and Data Science, Chen, D. and Jin, Z. and Li, G. and Li, Y. and Liu, A. and Zhao, Y. Eds. Springer. New York. (*co-first author). ([main paper], [bibtex])

  8. Leon-Novelo L., Womack, A., Zhu, H. and Wu, X. (2017) A Bayesian analysis of quantal bioassay experiments incorporating historical controls via Bayes factors, Statistics in Medicine, 36(12) 1907-1923. ([link], [bibtex])

  9. Zhang, L., Baladandayuthapani, V., Zhu, H., Baggerly, K. A., Czerniak, B. A. and Morris, J. S. (2016) Functional CAR models for spatially correlated high-dimensional functional data, Journal of the American Statistical Association--Theory and Methods, 111(514) 772--786.([link], [bibtex])

  10. Zhu, H. , Strawn, N. and Dunson, D. B. (2016) Bayesian graphical models for multivariate functional data, Journal of Machine Learning Research, 17 (204) 1-27 ([main paper], [supp. material], [code], [bibtex])

  11. Yang, J., Zhu, H., Choi, T. and Cox, D. D. (2016) Smoothing and mean-covariance estimation of functional data with a Bayesian hierarchical model, Bayesian Analysis 11(3) 649--670. ([main paper], [bibtex])

  12. Zhu, H., Yao, F. and Zhang, H. H. (2014) Structured functional additive regression in reproducing kernel Hilbert spaces, Journal of the Royal Statistical Society: Series B, 76, Part3, pp. 581-603 ([main paper], [bibtex])

  13. Zhu, H., Brown, P. J. and Morris, J. S. (2012) Robust classification of functional and quantitative image data using functional mixed models, Biometrics,68(4) 1260--1268. ( [main paper], [supp. material], [bibtex])

  14. Wei, F. and Zhu, H. (2012) Group coordinate descent algorithms for nonconvex penalized regression, Computational Statistics and Data Analysis 56(2) 316--326. ([link], [bibtex])

  15. Zhu, H. , Brown, P. J. and Morris, J. S. (2011) Robust, adaptive functional regression in functional mixed model framework, Journal of the American Statistical Association -Theory and Methods , 106 (495) 1167-1179. ([main paper], [supp. material], [bibtex])

  16. Zhu, H., Vannucci, M. and Cox, D. D. (2010) A Bayesian Hierarchical Model for Classification with Selection of Functional Predictors,Biometrics, 66 463-473.([main paper], [supp. material], [bibtex])

  17. Zhu, H. and Cox, D. D. (2009) A Functional Generalized Linear Model with Curve Selection in Cervical Pre-Cancer Diagnosis using Fluorescence Spectroscopy,Optimality: The Third Erich L. Lehmann Symposium . 57 173-189.([main paper], [bibtex])

    18. Peer Reviewed —Interdisciplinary

  18. Zhu, H. , Gupta, A. K., Wu, X., Goldsworthy, M., Wang, R., Mikkilineni, M., and Mueller, R. (2023) A validation study for a bat-inspired sonar sensing simulator, PLoS ONE 18(1):e0280631. doi:10.1371/journal.pone.0280631. ([link], [bibtex])

  19. Tanveer, M. H., Thomas, A., Ahmed, W. and Zhu, H. (2021) Estimate the unknown environment with biosonar echoes — a simulation study, Sensors 21(12) 4186. doi:10.3390/s21124186. ([link], [bibtex])

  20. Tanveer, M. H., Wu, X., Thomas, A., Ming, C., Mueller, R., Tokekar, P. and Zhu, H. (2020) A simulation framework for bioinspired sonar sensing with Unmanned Aerial Vehicles, PLOS ONE 15(11): e0241443. doi: 10.1371/journal.pone.0241443. ([link], [bibtex])

  21. Banerjee, S., Zhu, H., Tang, M., Wu, X., Feng, W., and Xie, H. (2019) Identifying transcriptional regulatory modules among different chromatin states in mouse neural stem cells, Frontiers in Genetics 9:731. doi: 10.3389/fgene.2018.00731. ([link], [bibtex])

  22. Tang, M., Hasan, M.S., Zhang, L., Zhu, H., and Wu, X. (2019) vi-HMM: A novel HMM-based method for sequence variant identification in short read data, Human Genomics. 13, 9 (2019). ([link], [bibtex])

  23. Richey, J. A., Sullivan-Toole, H., Strege, M., Carlton, C., McDaniel, D., Komelski, M., Epperley, A., Zhu, H., Allen, I. C. (2019) Precision Implementation of Minimal Erythema Dose (MED) Testing to Assess Individual Variation in Human Inflammatory Response, Journal of Visualized Experiments. (152) e59813. doi:10.3791/59813. ([link], [bibtex])

  24. Tran, H., Zhu, H., Wu, X., Kim, G., Clarke, C. R., Larose, H., Haak, D. C., Askew, S. D., Barney, J. N., Westwood, J. H. and Zhang, L. (2018) Identification of Differentially Methylated Sites with Weak Methylation Effects. Genes. 9 (2) 75. ([link], [bibtex])

  25. Bukvic, A, Zhu, H., Lavoie, R. and Becker, A. (2018) The role of proximity to waterfront in residents' relocation decision-making post-Hurricane Sandy. Ocean & Coastal Management. 154 8-19. ([link], [bibtex])

  26. Ming, C., Zhu, H. and Müller, R. (2017) A Simplified Model of Biosonar Echoes from Foliage and the Properties of Natural Foliages. PLoS ONE. 12 (12), e0189824. ([link], [bibtex])

  27. Müller, R., Gupta, A. K., Zhu, H., Pannala, M., Gillani, U. S., Fu, Y., Caspers, P. and Buck, J. R. (2017) Dynamic substrate for the physical encoding of sensory information in bat biosonar, Physical Review Letters. 118 (158102) 1--5. ([link], [bibtex])

  28. Ming, C., Gupta, A. K., Lu, R., Zhu, H. and Müller, R. (2017) A computational model for biosonar echoes from foliage, PLOS ONE. 12 (8) 1-18. ([link], [bibtex])

  29. Sun, M., Sun, Z., Wu, X., Rajaram, V., Keimig, D., Lim, J., Zhu, H. and Xie, H. (2016) Mammalian brain development is accompanied by a dramatic increase in Bipolar DNA methylation, Scientific Reports. 6(32998) 1-11. ([link], [bibtex])

  30. Wu, X. and Zhu, H. (2015) A Bayesian analysis of copy number variations in array comparative genomic hybridization data. International Journal of Biomedical Data Mining. 4(116) 1--12. ([main paper], [bibtex])

  31. Karunasena, E., McIver, L.J., Bavarva, J. H., Wu, X. Zhu, H. and Garner, H.R. (2015) 'Cut from the Same Cloth': Shared Microsatellite Variants among Cancers Link Origins to the Neural Crest, Oncotarget. 6(26) 22038-47. ([link], [bibtex])

  32. Wu, X. and Zhu, H. (2015) Fast maximum likelihood estimation of mutation rates using a birth-death process, Journal of Theoretical Biology. 366 1-7. ([link], [bibtex])

  33. Wu, X., Sun, M., Zhu, H. and Xie, H. (2015) Nonparametric Bayesian Clustering to Detect Bipolar Methylated Genomic Loci, BMC Bioinformatics. 16:11 ([link], [bibtex])

  34. Karunasena, E., McIver, L.J., Rood, B. R., Wu, X., Zhu, H., Bavarva, J.H. and Garner, H.R. (2014) Somatic Microsatellite Loci Differentiate Glioblastoma Multiforme from Lower-Grade Gliomas, Oncogene. 5(15) 6003-6014. ([link], [bibtex])

    35. Peer Reviewed—Conference Proceedings

  35. Wahad, M., Islam, M., Wu, X. and Zhu, H. (2022) Fast Simulation of Trees and Forests for Bat-inspired Sonar Sensing. The 5th International Conference on Information and Computer Technologies (ICICT) . ([link], [code],[bibtex])

  36. Tanveer, M. H., Zhu, H., Ahmed, W., Thomas, A., Imran, B. M., Salman M. (2021) Mel-spectrogram and Deep CNN based Representation Learning from Bio-Sonar Implementation on UAVs. 2021 International Conference on Computer, Control and Robotics (ICCCR). ([link],[bibtex])

  37. Tanveer, M. H., Thomas, A., Wu, X. and Zhu, H. (2020). Simulate Forest Trees by Integrating L-System and 3D CAD Files. 2020 3rd International Conference on Information and Computer Technologies (ICICT). ([link], [bibtex])

  38. Tanveer, M. H., Thomas, A., Wu, X., Mueller, R., Tokekar, P. and Zhu, H. (2020). Recreating Bat Behavior on Quad-Rotor UAVs—A Simulation Approach. The Thirty-Third International FLAIRS Conference (FLAIRS-33). ([link], [bibtex])

    39. Other Conference Proceedings

  39. Zhu, H., Vannucci, M. and Cox, D. D. (2007) Functional Data Classification in Cervical Pre-cancer Diagnosis: A Bayesian Variable Selection Model Joint Statistical Meetings Proceedings 2007 ( [main paper], [bibtex]).