The BRADLEY DEPARTMENT of ELECTRICAL and COMPUTER ENGINEERING

Jason Jianhua Xuan | ECE | Virginia Tech

ECE PROFILE

Jason Jianhua Xuan

Picture of Jason Jianhua Xuan

Office:
Virginia Tech Research Center - Arlington

Mailing Address:
Virginia Tech Research Center - Arlington
Room 4-030900 N. Glebe Road
Arlington, VA 22203

(571) 858-3151
(571) 858-3015
xuan@vt.edu


Education:

Ph.D., Electrical Engineering, University of Maryland, Baltimore County, USA, 1997
Ph.D., Electronics and Communications Engineering, Zhejiang University, China, 1991
M.S., Electronics and Communications Engineering, Zhejiang University, China, 1988
B.S., Electronics and Communications Engineering, Zhejiang University, China, 1985

Teaching Interests:

Deep learning, machine learning, computer vision, digital image processing, medical imaging and image analysis, artificial intelligence, multimedia signal processing, digital signal processing, signals & systems, statistical learning for bioinformatics, computational systems biology, information visualization, computer graphics, pattern recognition, and information retrieval.

Research Interests:

Deep learning & artificial intelligence, theory of deep learning, GPU-based implementation of deep learning systems, neuroscience-inspired deep learning algorithms, deep genomics, deep proteomics, computational systems biology, bioinformatics for cancer research, advanced medical image analysis, and image guided radiation therapy.

Grants and Projects:
  • Uncovering Estrogen Receptor-Signaling Networks to Overcome Endocrine Resistance (NIH/NCI R01CA149653; PI)
  • Transgenerational Effects of Maternal High Fat Diet During Pregnancy of Breast Cancer (NIH/NCI R01CA164384; Co-PI)
  • Collaborative research in integrative cancer biology (NIH/NCI U01CA184902; Co-I)
Selected Publications:
  • Title: ChIP-BIT: Bayesian inference of target genes using a novel joint probabilistic model of ChIP-seq profiles
    Author(s): X. Chen, J.-G. Jung, A. N. Shajahan-Haq, R. Clarke, I.-M. Shih, Y. Wang, L. Magnani, T.-L. Wang, and J. Xuan*
    Journal: Nucleic Acids Res. (Impact Factor (IF) = 10.162), 44(7):e65, 2016
  • Title: Identifying protein interaction subnetworks by a bagging Markov random field-based method
    Author(s): L. Chen, J. Xuan*, R. B. Riggins, Y. Wang and R. Clarke
    Journal: Nucleic Acids Res. (IF = 8.808), 41(2):e42, 2013
  • Title: IntAPT: Integrated assembly of phenotype-specific transcripts from multiple RNA-seq profiles
    Author(s): X. Shi*, A. F. Neuwald, X. Wang, T.-L. Wang, L. Hilakivi-Clarke, R. Clarke, and J. Xuan*
    Journal: Bioinformatics (IF = 5.610), 2020
  • Title: SprseIso: A novel Bayesian approach to identify alternatively spliced isoforms from RNA-seq data
    Author(s): X. Shi, X. Wang, T.-L. Wang, L. Hilakivi-Clarke, R. Clarke, and J. Xuan*
    Journal: Bioinformatics (IF = 4.531), 34(1): 56-63, 2018
  • Title: CRNET: An efficient sampling approach to infer functional regulatory networks by integrating large-scale ChIP-seq and time-course RNA-seq data
    Author(s): X. Chen, J. Gu, X. Wang, J.-G. Jung, T.-L. Wang, L. Hilakivi-Clarke, R. Clarke, and J. Xuan*
    Journal: Bioinformatics (IF = 4.531), 34(10): 1733-1740, 2018
  • Title: PSSV: A novel pattern-based probabilistic approach for somatic structural variation identification
    Author(s): X. Chen, X. Shi, L. Hilakivi-Clarke, A. N. Shajahan-Haq, R. Clarke, and J. Xuan*
    Journal: Bioinformatics (IF = 5.481), 33(2):177-183, 2017
  • Title: DM-BLD: Differential methylation detection using a hierarchical Bayesian model exploiting local dependency
    Author(s): X. Wang, J. Gu, L. Hilakivi-Clarke, R. Clarke, and J. Xuan*
    Journal: Bioinformatics (IF = 5.481), 33(2):161-168, 2017
  • Title: BMRF-Net: a software tool for identification of protein interaction subnetworks by a bagging Markov random field-based method
    Author(s): X. Shi, R. O. Barnes, L. Chen, A. N. Shajahan-Haq, L. Hilakivi-Clarke, R. Clarke, Y. Wang, J. Xuan*
    Journal: Bioinformatics (IF=5.766), 31(14):2412-2414, 2015
  • Title: Robust identification of transcriptional regulatory networks using a Gibbs sampler on outlier sum statistic
    Author(s): J. Gu, J. Xuan*, R. B. Riggins, L. Chen, Y. Wang, and R. Clarke
    Journal: Bioinformatics (IF=5.323), 28(15):1990-1997, 2012
  • Title: Multilevel support vector regression analysis to identify condition-specific regulatory networks
    Author(s): L. Chen, J. Xuan*, R. B. Riggins, Y. Wang, E. P. Hoffman, and R. Clarke
    Journal: Bioinformatics (IF = 4.877), 26(11):1416-1422, 2010