headshot

Heng Huang

Professor
John A. Jurenko Endowed Professor
Google Scholar Electrical and Computer Engineering

overview

Dr. Huang has published more than 130 papers in top-tier conferences and many papers in premium journals such as NIPS, ICML, KDD, RECOMB, ISMB, IJCAI, AAAI, CVPR, ICCV, SIGIR, Bioinformatics, IEEE Trans. On Medical Imaging, Medical Image Analysis, IEEE TKDE, and others. As principal investigator, is leading a National Institutes of Health-funded $2 million R01 project on imaging genomics based complex brain disorder study, multiple NSF-funded projects on precision medicine, biomedical data science, big data mining, electronic medical record data mining and privacy-preserving, computational biology, smart healthcare, cyber physical systems, and also industry-funded projects on computational sustainability, smart metering, and smart grids.

about

PhD, Computer Science, Dartmouth College

MS, BS, Computer Science, Shanghai Jiao Tong University

Chen, H., Wang, Y., Zheng, F., Deng, C., & Huang, H. (2021). Sparse Modal Additive Model. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 32(6), 2373-2387.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TNNLS.2020.3005144.

Dizaji, K.G., Chen, W., & Huang, H. (2021). Deep Large-Scale Multitask Learning Network for Gene Expression Inference. JOURNAL OF COMPUTATIONAL BIOLOGY, 28(5), 485-500.Mary Ann Liebert Inc. doi: 10.1089/cmb.2020.0438.

Gu, B., Geng, X., Li, X., Shi, W., Zheng, G., Deng, C., & Huang, H. (2021). Scalable Kernel Ordinal Regression via Doubly Stochastic Gradients. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 32(8), 3677-3689.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TNNLS.2020.3015937.

Gu, B., Xu, A., Huo, Z., Deng, C., & Huang, H. (2021). Privacy-Preserving Asynchronous Vertical Federated Learning Algorithms for Multiparty Collaborative Learning. IEEE Transactions on Neural Networks and Learning Systems, 1-13.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/tnnls.2021.3072238.

Gu, B., Zhai, Z., Deng, C., & Huang, H. (2021). Efficient Active Learning by Querying Discriminative and Representative Samples and Fully Exploiting Unlabeled Data. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 32(9), 4111-4122.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TNNLS.2020.3016928.

Liu, D., Zhang, D., Song, Y., Huang, H., & Cai, W. (2021). Panoptic Feature Fusion Net: A Novel Instance Segmentation Paradigm for Biomedical and Biological Images. IEEE TRANSACTIONS ON IMAGE PROCESSING, 30, 2045-2059.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TIP.2021.3050668.

Liu, D., Zhang, D., Song, Y., Zhang, F., O'Donnell, L., Huang, H., Chen, M., & Cai, W. (2021). PDAM: A Panoptic-Level Feature Alignment Framework for Unsupervised Domain Adaptive Instance Segmentation in Microscopy Images. IEEE TRANSACTIONS ON MEDICAL IMAGING, 40(1), 154-165.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TMI.2020.3023466.

Lu, L., Elbeleidy, S., Baker, L., Wang, H., Shen, L., Heng, H., & ADNI. (2021). Improved Prediction of Cognitive Outcomes via Globally Aligned Imaging Biomarker Enrichments Over Progressions. IEEE TRANSACTIONS ON BIOMEDICAL ENGINEERING, 68(11), 3336-3346.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TBME.2021.3070875.

Nie, F., Tian, L., Huang, H., & Ding, C. (2021). Non-Greedy L21-Norm Maximization for Principal Component Analysis. IEEE TRANSACTIONS ON IMAGE PROCESSING, 30, 5277-5286.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TIP.2021.3073282.

Shi, W., Gu, B., Li, X., Deng, C., & Huang, H. (2021). Triply stochastic gradient method for large-scale nonlinear similar unlabeled classification. MACHINE LEARNING, 110(8), 2005-2033.Springer Science and Business Media LLC. doi: 10.1007/s10994-021-05980-1.

Yan, Q., Jiang, Y., Huang, H., Swaroop, A., Chew, E.Y., Weeks, D.E., Chen, W., & Ding, Y. (2021). Genome-Wide Association Studies-Based Machine Learning for Prediction of Age-Related Macular Degeneration Risk. TRANSLATIONAL VISION SCIENCE & TECHNOLOGY, 10(2), 29.Association for Research in Vision and Ophthalmology (ARVO). doi: 10.1167/tvst.10.2.29.

Zhang, Q., Huang, F., Deng, C., & Huang, H. (2021). Faster Stochastic Quasi-Newton Methods. IEEE Transactions on Neural Networks and Learning Systems, 1-10.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/tnnls.2021.3056947.

Brand, L., Nichols, K., Wang, H., Shen, L., Huang, H., & ADNI. (2020). Joint Multi-Modal Longitudinal Regression and Classification for Alzheimer's Disease Prediction. IEEE TRANSACTIONS ON MEDICAL IMAGING, 39(6), 1845-1855.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TMI.2019.2958943.

Dang, Z., Gu, B., & Huang, H. (2020). Large-Scale Kernel Method for Vertical Federated Learning. In Lecture Notes in Computer Science. 12500 LNCS, (pp. 66-80).Springer International Publishing. doi: 10.1007/978-3-030-63076-8_5.

Dong, Q., Ge, F., Ning, Q., Zhao, Y., Lv, J., Huang, H., Yuan, J., Jiang, X., Shen, D., & Liu, T. (2020). Modeling Hierarchical Brain Networks via Volumetric Sparse Deep Belief Network. IEEE Trans Biomed Eng, 67(6), 1739-1748.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TBME.2019.2945231.

Geng, Y.A., Li, Q., Liang, M., Chi, C.Y., Tan, J., & Huang, H. (2020). Local-Density Subspace Distributed Clustering for High-Dimensional Data. IEEE TRANSACTIONS ON PARALLEL AND DISTRIBUTED SYSTEMS, 31(8), 1799-1814.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TPDS.2020.2975550.

Ghasedi Dizaji, K., Gao, H., Yang, Y., Huang, H., & Deng, C. (2020). Robust Cumulative Crowdsourcing Framework Using New Incentive Payment Function and Joint Aggregation Model. IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 31(11), 4610-4621.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TNNLS.2019.2956523.

Gu, B., Xian, W., Huo, Z., Deng, C., & Huang, H. (2020). A Unified q-Memorization Framework for Asynchronous Stochastic Optimization. JOURNAL OF MACHINE LEARNING RESEARCH, 21.

Jia, H., Xia, Y., Song, Y., Zhang, D., Huang, H., Zhang, Y., & Cai, W. (2020). 3D APA-Net: 3D Adversarial Pyramid Anisotropic Convolutional Network for Prostate Segmentation in MR Images. IEEE TRANSACTIONS ON MEDICAL IMAGING, 39(2), 447-457.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TMI.2019.2928056.

Russo, D.P., Yan, X., Shende, S., Huang, H., Yan, B., & Zhu, H. (2020). Virtual Molecular Projections and Convolutional Neural Networks for the End-to-End Modeling of Nanoparticle Activities and Properties. ANALYTICAL CHEMISTRY, 92(20), 13971-13979.American Chemical Society (ACS). doi: 10.1021/acs.analchem.0c02878.

Wang, X., Sun, Z., Zhang, Y., Xu, Z., Xin, H., Huang, H., Duerr, R.H., Chen, K., Ding, Y., & Chen, W. (2020). BREM-SC: a bayesian random effects mixture model for joint clustering single cell multi-omics data. NUCLEIC ACIDS RESEARCH, 48(11), 5814-5824.Oxford University Press (OUP). doi: 10.1093/nar/gkaa314.

Zhang, W., Zhao, S., Hu, X., Dong, Q., Huang, H., Zhang, S., Zhao, Y., Dai, H., Ge, F., Guo, L., & Liu, T. (2020). Hierarchical Organization of Functional Brain Networks Revealed by Hybrid Spatiotemporal Deep Learning. Brain Connect, 10(2), 72-82.Mary Ann Liebert Inc. doi: 10.1089/brain.2019.0701.

Zheng, S., Ding, C., Nie, F., & Huang, H. (2019). Harmonic Mean Linear Discriminant Analysis. IEEE Transactions on Knowledge and Data Engineering, 31(8), 1520-1531.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/tkde.2018.2861858.

Zhu, D., Yan, J., Huang, H., Shen, L., Thompson, P.M., & Westin, C.F. (2019). Multimodal Brain Image Analysis (MBIA). 11846 LNCS.

Nha Nguyen, An Vo, Haibin Sun, & Heng Huang. (2018). Heavy-Tailed Noise Suppression and Derivative Wavelet Scalogram for Detecting DNA Copy Number Aberrations. IEEE/ACM Trans Comput Biol Bioinform, 15(5), 1625-1635.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TCBB.2017.2723884.

Yan, J., Du, L., Kim, S., Risacher, S.L., Huang, H., Inlow, M., Moore, J.H., Saykin, A.J., & Shen, L. (2018). Bootstrapped Sparse Canonical Correlation Analysis. In Imaging Genetics. (pp. 101-117).Elsevier. doi: 10.1016/b978-0-12-813968-4.00006-7.

Wang, H., Yan, L., Huang, H., & Ding, C. (2017). From Protein Sequence to Protein Function via Multi-Label Linear Discriminant Analysis. IEEE/ACM Trans Comput Biol Bioinform, 14(3), 503-513.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TCBB.2016.2591529.

Chang, X., Nie, F., Yang, Y., Zhang, C., & Huang, H. (2016). Convex Sparse PCA for Unsupervised Feature Learning. ACM Transactions on Knowledge Discovery from Data, 11(1), 1-16.Association for Computing Machinery (ACM). doi: 10.1145/2910585.

Du, L., Huang, H., Yan, J., Kim, S., Risacher, S., Inlow, M., Moore, J., Saykin, A., Shen, L., & Alzheimer’s Disease Neuroimaging Initiative. (2016). Structured sparse CCA for brain imaging genetics via graph OSCAR. BMC Syst Biol, 10 Suppl 3(S3), 68.Springer Science and Business Media LLC. doi: 10.1186/s12918-016-0312-1.

Du, L., Huang, H., Yan, J., Kim, S., Risacher, S.L., Inlow, M., Moore, J.H., Saykin, A.J., Shen, L., & Alzheimer’s Disease Neuroimaging Initiative. (2016). Structured sparse canonical correlation analysis for brain imaging genetics: an improved GraphNet method. Bioinformatics, 32(10), 1544-1551.Oxford University Press (OUP). doi: 10.1093/bioinformatics/btw033.

Wang, X., Lee, W.J., Huang, H., Szabados, R.L., Wang, D.Y., & Van Olinda, P. (2016). Factors that Impact the Accuracy of Clustering-Based Load Forecasting. IEEE Transactions on Industry Applications, 52(5), 3625-3630.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/tia.2016.2558563.

Lv, J., Jiang, X., Li, X., Zhu, D., Chen, H., Zhang, T., Zhang, S., Hu, X., Han, J., Huang, H., Zhang, J., Guo, L., & Liu, T. (2015). Sparse representation of whole-brain fMRI signals for identification of functional networks. Med Image Anal, 20(1), 112-134.Elsevier BV. doi: 10.1016/j.media.2014.10.011.

Nie, F., Wang, H., Huang, H., & Ding, C. (2015). Joint Schatten $$p$$ p -norm and $$\ell _p$$ ℓ p -norm robust matrix completion for missing value recovery. Knowledge and Information Systems, 42(3), 525-544.Springer Science and Business Media LLC. doi: 10.1007/s10115-013-0713-z.

Song, Y., Cai, W., Huang, H., Zhou, Y., Wang, Y., & Feng, D.D. (2015). Locality-constrained Subcluster Representation Ensemble for lung image classification. Med Image Anal, 22(1), 102-113.Elsevier BV. doi: 10.1016/j.media.2015.03.003.

Wang, D., Nie, F., & Huang, H. (2015). Feature Selection via Global Redundancy Minimization. IEEE Transactions on Knowledge and Data Engineering, 27(10), 2743-2755.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/tkde.2015.2426703.

Wang, H., Huang, H., & Ding, C. (2015). Correlated Protein Function Prediction via Maximization of Data-Knowledge Consistency. J Comput Biol, 22(6), 546-562.Mary Ann Liebert Inc. doi: 10.1089/cmb.2014.0172.

Wang, H., Nie, F., & Huang, H. (2015). Large-Scale Cross-Language Web Page Classification via Dual Knowledge Transfer Using Fast Nonnegative Matrix Trifactorization. ACM Transactions on Knowledge Discovery from Data, 10(1), 1-29.Association for Computing Machinery (ACM). doi: 10.1145/2710021.

Yan, J., Li, T., Wang, H., Huang, H., Wan, J., Nho, K., Kim, S., Risacher, S.L., Saykin, A.J., Shen, L., & Alzheimer's Disease Neuroimaging Initiative. (2015). Cortical surface biomarkers for predicting cognitive outcomes using group l2,1 norm. Neurobiol Aging, 36 Suppl 1(S1), S185-S193.Elsevier BV. doi: 10.1016/j.neurobiolaging.2014.07.045.

De, W., Wang, Y., Nie, F., Yan, J., Cai, W., Saykin, A.J., Shen, L., & Huang, H. (2014). Human connectome module pattern detection using a new multi-graph MinMax cut model. Med Image Comput Comput Assist Interv, 17(Pt 3), 313-320.Springer International Publishing. doi: 10.1007/978-3-319-10443-0_40.

Huang, J., Nie, F., Huang, H., & Ding, C. (2014). Robust Manifold Nonnegative Matrix Factorization. ACM Transactions on Knowledge Discovery from Data, 8(3), 1-21.Association for Computing Machinery (ACM). doi: 10.1145/2601434.

Metsis, V., Makedon, F., Shen, D., & Huang, H. (2014). DNA Copy Number Selection Using Robust Structured Sparsity-Inducing Norms. IEEE/ACM Trans Comput Biol Bioinform, 11(1), 168-181.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TCBB.2013.141.

Song, Y., Cail, W., Huang, H., Zhou, Y., Feng, D.D., & Chen, M. (2014). Large margin aggregation of local estimates for medical image classification. Med Image Comput Comput Assist Interv, 17(Pt 2), 196-203.Springer International Publishing. doi: 10.1007/978-3-319-10470-6_25.

Wang, H., Huang, H., & Makedon, F. (2014). Emotion Detection via Discriminant Laplacian Embedding. Universal Access in the Information Society, 13(1), 23-31.Springer Science and Business Media LLC. doi: 10.1007/s10209-013-0312-5.

Wu, G., Wang, Q., Zhang, D., Nie, F., Huang, H., & Shen, D. (2014). A generative probability model of joint label fusion for multi-atlas based brain segmentation. Med Image Anal, 18(6), 881-890.Elsevier BV. doi: 10.1016/j.media.2013.10.013.

Yang Song, Weidong Cai, Heng Huang, Xiaogang Wang, Yun Zhou, Fulham, M.J., & Feng, D.D. (2014). Lesion detection and characterization with context driven approximation in thoracic FDG PET-CT images of NSCLC studies. IEEE Trans Med Imaging, 33(2), 408-421.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TMI.2013.2285931.

Zhang, F., Song, Y., Cai, W., Lee, M.Z., Zhou, Y., Huang, H., Shan, S., Fulham, M.J., & Feng, D.D. (2014). Lung nodule classification with multilevel patch-based context analysis. IEEE Trans Biomed Eng, 61(4), 1155-1166.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/TBME.2013.2295593.

Zhang, Z., Huang, H., & Shen, D. (2014). Integrative analysis of multi-dimensional imaging genomics data for Alzheimer's disease prediction. Frontiers in Aging Neuroscience, 6(SEP).Frontiers Media SA. doi: 10.3389/fnagi.2014.00260.

De, W., Nie, F., Huang, H., Yan, J., Risacher, S.L., Saykin, A.J., & Shen, L. (2013). Structural brain network constrained neuroimaging marker identification for predicting cognitive functions. Inf Process Med Imaging, 23, 536-547.Springer Berlin Heidelberg. doi: 10.1007/978-3-642-38868-2_45.

Huang, J., Nie, F., Huang, H., Tu, Y.C., & Lei, Y. (2013). Social trust prediction using heterogeneous networks. ACM Transactions on Knowledge Discovery from Data, 7(4), 1-21.Association for Computing Machinery (ACM). doi: 10.1145/2541268.2541270.

Luo, D., Ding, C., & Huang, H. (2013). Toward structural sparsity: an explicit $$\ell _{2}/\ell _0$$ approach. Knowledge and Information Systems, 36(2), 411-438.Springer Science and Business Media LLC. doi: 10.1007/s10115-012-0545-2.

Shen, L., Liu, T., Yap, P.T., Huang, H., Shen, D., & Westin, C.F. (2013). Multimodal Brain Image Analysis. 8159 LNCS.Springer International Publishing. doi: 10.1007/978-3-319-02126-3.

Wang, H., Huang, H., & Ding, C. (2013). Function-function correlated multi-label protein function prediction over interaction networks. J Comput Biol, 20(4), 322-343.Mary Ann Liebert Inc. doi: 10.1089/cmb.2012.0272.

Wang, H., Huang, H., Ding, C., & Nie, F. (2013). Predicting protein-protein interactions from multimodal biological data sources via nonnegative matrix tri-factorization. J Comput Biol, 20(4), 344-358.Mary Ann Liebert Inc. doi: 10.1089/cmb.2012.0273.

Cai, X., Wang, H., Huang, H., & Ding, C. (2012). Joint stage recognition and anatomical annotation of Drosophila gene expression patterns. Bioinformatics, 28(12), i16-i24.Oxford University Press (OUP). doi: 10.1093/bioinformatics/bts220.

Metsis, V., Huang, H., Andronesi, O.C., Makedon, F., & Tzika, A. (2012). Heterogeneous data fusion for brain tumor classification. Oncol Rep, 28(4), 1413-1416.Spandidos Publications. doi: 10.3892/or.2012.1931.

Sircar, K., Huang, H., Hu, L., Cogdell, D., Dhillon, J., Tzelepi, V., Efstathiou, E., Koumakpayi, I.H., Saad, F., Luo, D., Bismar, T.A., Aparicio, A., Troncoso, P., Navone, N., & Zhang, W. (2012). Integrative molecular profiling reveals asparagine synthetase is a target in castration-resistant prostate cancer. Am J Pathol, 180(3), 895-903.Elsevier BV. doi: 10.1016/j.ajpath.2011.11.030.

Sircar, K., Huang, H., Hu, L., Liu, Y., Dhillon, J., Cogdell, D., Aprikian, A., Efstathiou, E., Navone, N., Troncoso, P., & Zhang, W. (2012). Mitosis phase enrichment with identification of mitotic centromere-associated kinesin as a therapeutic target in castration-resistant prostate cancer. PLoS One, 7(2), e31259.Public Library of Science (PLoS). doi: 10.1371/journal.pone.0031259.

Wang, H., Nie, F., Huang, H., Kim, S., Nho, K., Risacher, S.L., Saykin, A.J., Shen, L., & Alzheimer's Disease Neuroimaging Initiative. (2012). Identifying quantitative trait loci via group-sparse multitask regression and feature selection: an imaging genetics study of the ADNI cohort. Bioinformatics, 28(2), 229-237.Oxford University Press (OUP). doi: 10.1093/bioinformatics/btr649.

Wang, H., Nie, F., Huang, H., Risacher, S.L., Saykin, A.J., Shen, L., & Alzheimer's Disease Neuroimaging Initiative. (2012). Identifying disease sensitive and quantitative trait-relevant biomarkers from multidimensional heterogeneous imaging genetics data via sparse multimodal multitask learning. Bioinformatics, 28(12), i127-i136.Oxford University Press (OUP). doi: 10.1093/bioinformatics/bts228.

Wang, H., Nie, F., Huang, H., Yan, J., Kim, S., Nho, K., Risacher, S.L., Saykin, A.J., Shen, L., & Alzheimer's Disease Neuroimaging Initiative. (2012). From phenotype to genotype: an association study of longitudinal phenotypic markers to Alzheimer's disease relevant SNPs. Bioinformatics, 28(18), i619-i625.Oxford University Press (OUP). doi: 10.1093/bioinformatics/bts411.

Zhang, N., O’Neill, L., Das, G., Cheng, X., & Huang, H. (2012). No Silver Bullet. International Journal of Healthcare Information Systems and Informatics, 7(4), 48-58.IGI Global. doi: 10.4018/jhisi.2012100104.

Nguyen, N., Huang, H., Oraintara, S., & Vo, A. (2010). Stationary wavelet packet transform and dependent laplacian bivariate shrinkage estimator for array-CGH data smoothing. J Comput Biol, 17(2), 139-152.Mary Ann Liebert Inc. doi: 10.1089/cmb.2009.0013.

Metsis, V., Huang, H., Makedon, F., & Tzika, A. (2009). Heterogeneous Data Fusion to Type Brain Tumor Biopsies. IFIP International Federation for Information Processing, 296, 233-240.Springer US. doi: 10.1007/978-1-4419-0221-4_28.

Huang, H., Shen, L., Ford, J., Wang, Y.H., & Xu, Y.R. (2008). Computational Issues in Biomedical Nanometrics and Nano-Materials. Journal of Nano Research, 1(1), 50-58.Trans Tech Publications, Ltd. doi: 10.4028/www.scientific.net/jnanor.1.50.

Nguyen, N., Huang, H., Oraintara, S., & Vo, A. (2008). GaborLocal: peak detection in mass spectrum by Gabor filters and Gaussian local maxima. Comput Syst Bioinformatics Conf, 7, 85-96.PUBLISHED BY IMPERIAL COLLEGE PRESS AND DISTRIBUTED BY WORLD SCIENTIFIC PUBLISHING CO. doi: 10.1142/9781848162648_0008.

Huang, H., Shen, L., & Nguyen, N. (2007). Three-dimensional Models for Cardiac Bioelectricity Simulation: Cell to Organ. SIMULATION, 83(4), 321-327.SAGE Publications. doi: 10.1177/0037549707083112.

Huang, H., Shen, L., Zhang, R., Makedon, F., Saykin, A., & Pearlman, J. (2007). A novel surface registration algorithm with biomedical modeling applications. IEEE Trans Inf Technol Biomed, 11(4), 474-482.Institute of Electrical and Electronics Engineers (IEEE). doi: 10.1109/titb.2007.897577.

Huang, H., Shen, L., Ford, J., Gao, L., & Pearlman, J. (2006). Early lung cancer detection based on registered perfusion MRI. Oncol Rep, 15 Spec no.(4), 1081-1084.Spandidos Publications. doi: 10.3892/or.15.4.1081.

Huang, H., Shen, L., Zhang, R., Makedon, F., Hettleman, B., & Pearlman, J. (2006). Cardiac motion analysis to improve pacing site selection in CRT. Acad Radiol, 13(9), 1124-1134.Elsevier BV. doi: 10.1016/j.acra.2006.07.010.

Shen, L., Zheng, W., Gao, L., Huang, H., Makedon, F., & Pearlman, J. (2006). Spatio-temporal modeling of lung images for cancer detection. Oncol Rep, 15 Spec no.(4), 1085-1089.Spandidos Publications. doi: 10.3892/or.15.4.1085.

Huang, H., Shen, L., Zhang, R., Makedon, F., Hettleman, B., & Pearlman, J. (2005). Surface alignment of 3D spherical harmonic models: application to cardiac MRI analysis. Med Image Comput Comput Assist Interv, 8(Pt 1), 67-74.Springer Berlin Heidelberg. doi: 10.1007/11566465_9.

Jia, H., Cai, W., Huang, H., & Xia, Y. (2021). H$$^2$$NF-Net for Brain Tumor Segmentation Using Multimodal MR Imaging: 2nd Place Solution to BraTS Challenge 2020 Segmentation Task. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12659 LNCS, (pp. 58-68).Springer International Publishing. doi: 10.1007/978-3-030-72087-2_6.

Bao, R., Gu, B., & Huang, H. (2020). Fast oscar and owl regression via safe screening rules. In 37th International Conference on Machine Learning, ICML 2020, PartF168147-1, (pp. 630-640).

Brand, L., Nichols, K., Wang, H., Huang, H., Shen, L., & Alzheimer’s Disease Neuroimaging Initiative. (2020). Predicting Longitudinal Outcomes of Alzheimer's Disease via a Tensor-Based Joint Classification and Regression Model. In Pac Symp Biocomput, 25(2020), (pp. 7-18).United States.

Chen, Y., Tang, H., Guo, L., Peven, J.C., Huang, H., Leow, A.D., Lamar, M., & Zhan, L. (2020). A GENERALIZED FRAMEWORK OF PATHLENGTH ASSOCIATED COMMUNITY ESTIMATION FOR BRAIN STRUCTURAL NETWORK. In Proc IEEE Int Symp Biomed Imaging, 2020, (pp. 288-291).IEEE.United States. doi: 10.1109/isbi45749.2020.9098552.

Dang, Z., Deng, C., Yang, X., & Huang, H. (2020). Multi-Scale Fusion Subspace Clustering Using Similarity Constraint. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (pp. 6657-6666).IEEE. doi: 10.1109/cvpr42600.2020.00669.

Dizaji, K.G., Chen, W., & Huang, H. (2020). Deep Large-Scale Multi-task Learning Network for Gene Expression Inference. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12074 LNBI, (pp. 19-36).Springer International Publishing. doi: 10.1007/978-3-030-45257-5_2.

Ganjdanesh, A., Ghasedi, K., Zhan, L., Cai, W., & Huang, H. (2020). Predicting Potential Propensity of Adolescents to Drugs via New Semi-supervised Deep Ordinal Regression Model. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12261 LNCS, (pp. 635-645).Springer International Publishing. doi: 10.1007/978-3-030-59710-8_62.

Gao, H., & Huang, H. (2020). Can stochastic zeroth-order Frank-Wolfe method converge faster for non-convex problems?. In 37th International Conference on Machine Learning, ICML 2020, PartF168147-5, (pp. 3335-3344).

Gao, S., Huang, F., Pei, J., & Huang, H. (2020). Discrete Model Compression With Resource Constraint for Deep Neural Networks. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (pp. 1896-1905).IEEE. doi: 10.1109/cvpr42600.2020.00197.

Geng, Y.A., Li, Q., Lin, T., Zhang, J., Xu, L., Yao, W., Zheng, D., Lyu, W., & Huang, H. (2020). A Heterogeneous Spatiotemporal Network for Lightning Prediction. In 2020 IEEE International Conference on Data Mining (ICDM), 2020-November, (pp. 1034-1039).IEEE. doi: 10.1109/icdm50108.2020.00121.

Gu, B., Dang, Z., Li, X., & Huang, H. (2020). Federated Doubly Stochastic Kernel Learning for Vertically Partitioned Data. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, (pp. 2483-2493).ACM. doi: 10.1145/3394486.3403298.

Huang, F., Gao, S., Pei, J., & Huang, H. (2020). Momentum-Based policy gradient methods. In 37th International Conference on Machine Learning, ICML 2020, PartF168147-6, (pp. 4372-4383).

Jia, H., Xia, Y., Cai, W., & Huang, H. (2020). Learning High-Resolution and Efficient Non-local Features for Brain Glioma Segmentation in MR Images. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 12264 LNCS, (pp. 480-490).Springer International Publishing. doi: 10.1007/978-3-030-59719-1_47.

Li, J., & Huang, H. (2020). Faster Secure Data Mining via Distributed Homomorphic Encryption. In Proceedings of the 26th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, (pp. 2706-2714).ACM. doi: 10.1145/3394486.3403321.

Li, M., Deng, C., Li, T., Yan, J., Gao, X., & Huang, H. (2020). Towards Transferable Targeted Attack. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (pp. 638-646).IEEE. doi: 10.1109/cvpr42600.2020.00072.

Liu, D., Zhang, D., Song, Y., Zhang, F., ODonnell, L., Huang, H., Chen, M., & Cai, W. (2020). Unsupervised Instance Segmentation in Microscopy Images via Panoptic Domain Adaptation and Task Re-Weighting. In 2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), (pp. 4242-4251).IEEE. doi: 10.1109/cvpr42600.2020.00430.

Liu, G., Chen, H., & Huang, H. (2020). Sparse shrunk additive models. In 37th International Conference on Machine Learning, ICML 2020, PartF168147-8, (pp. 6150-6160).

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Huang, H., Shen, L., Zhang, R., Makedon, F., Hettleman, B., & Pearlman, J. (2005). A prediction framework for cardiac resynchronization therapy via 4D cardiac motion analysis. In Med Image Comput Comput Assist Interv, 8(Pt 1), (pp. 704-711).Springer Berlin Heidelberg.Germany. doi: 10.1007/11566465_87.

Huang, H., Shen, L., Zhang, R., Makedon, F., Hettleman, B., & Pearlman, J. (2005). Surface alignment of 3D spherical harmonic models: Application to cardiac MRI analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3749 LNCS, (pp. 67-74). doi: 10.1007/11566465_9.

Li, S., Zheng, W., Gao, L., Huang, H., Makedon, F., & Pearlman, J. (2005). Modeling time-intensity profiles for pulmonary nodules in MR images. In Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 7 VOLS, (pp. 1359-1362).

Shen, L., Gao, L., Zhuang, Z., DeMuinck, E., Huang, H., Makedon, F., & Pearlman, J. (2005). An interactive 3D visualization and manipulation tool for effective assessment of angiogenesis and arteriogenesis using computed tomographic angiography. In Medical Imaging 2005: Visualization, Image-Guided Procedures, and Display, 5744(II), (pp. 848-858).SPIE. doi: 10.1117/12.596138.

Xiong, F., Huang, H., Ford, J., Makedon, F.S., & Pearlman, J.D. (2005). A New Test System for Stability Measurement of Marker Gene Selection in DNA Microarray Data Analysis. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 3746 LNCS, (pp. 437-447).Springer Berlin Heidelberg. doi: 10.1007/11573036_41.

Huang, H., Makedon, F., Ford, J., Shen, L., Wang, Y., Steinberg, T., Gao, L., & Pearlman, J. (2004). Efficient similarity retrieval for temporal shape sequences: A case study using cardiac MR images. In Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 26 V, (pp. 3250-3253).

Wang, Y., Makedon, F., Ford, J., & Huang, H. (2004). A bipartite graph matching framework for finding correspondences between structural elements in two proteins. In Annual International Conference of the IEEE Engineering in Medicine and Biology - Proceedings, 26 IV, (pp. 2972-2975).

Research interests

Big Data Computing
Bioinformatics
Computer Vision
Data Mining
Health Informatics
Machine Learning
Medical Image Analysis
Neuroinformatics
NLP
Precision Medicine