Han Zhang
Department of Computer Science, Rutgers University,
110 Frelinghuysen Road, Piscataway, NJ 08854-8019
Email: han dot zhang at rutgers dot edu
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Han Zhang obtained his Ph.D. in Computer Science at Rutgers, the State University of New Jersey. He was working with Prof. Dimitris N. Metaxas in Computational Biomedicine Imaging and Modeling Center (CBIM).

His research interests are deep learning, computer vision, machine learning and medical image analysis.

This website is not maintained after graduation. Please come to this new personal website.

  • [02/2019] This website will not be maintained, come to this personal website.
  • [01/2019] Join Google Brain as a full-time Research Scientist.
  • [09/2018] Successfully defended my Ph.D. thesis.
  • [06/2018] Our StackGAN++ paper is accepted to T-PAMI.
  • [03/2018] Our AttnGAN paper is accepted to CVPR18.
  • [02/2018] Our OT-GAN paper is accepted to ICLR18.
  • [01/2018] I will join Google Brain as a research intern.
  • Our StackGAN paper is accepted as oral to ICCV2017.
  • October 2017,  I have been invited to present our StackGAN work on ICCV Tutorials on GANs.
  • Summer 2017,  I will join OpenAI as a research intern on generative models. 

  • 2012 ~ Present,   PhD in Computer Science, Rutgers University, NJ, USA
  • 2009 ~ 2012,      M.E. in Communication and Information Systems, BUPT, China
  • 2006 ~ 2009,      B.S. in Information Science, China Agricultural University, China

  • Working Experience
  • 01/2018 ~ 05/2018,   Research Intern, Google Brain, CA, USA.
  • 05/2017 ~ 08/2017,   Research Intern, OpenAI, CA, USA.
  • 05/2016 ~ 08/2016,   Core Data Science Intern, Facebook, CA, USA.
  • 05/2015 ~ 08/2015,   Software Engineering Intern, Philips Research North America, NY, USA.
  • 09/2011 ~ 02/2012,   Research Intern at Lab of Media Search, NUS, Singapore.
  • 08/2010 ~ 09/2010,   Software Engineering Intern, Samsung, Beijing, China.
  • Selected Publications
    (* indicates equal contributions)
    [Preprint] Self-Attention Generative Adversarial Networks.
    Han Zhang, Ian Goodfellow, Dimitris Metaxas, Augustus Odena.

    arXiv:1805.08318, 2018 [PDF]

    [ICLR'18] Improving GANs Using Optimal Transport
    Tim Salimans*, Han Zhang*, Alec Radford, Dimitris Metaxas
    ICLR, 2018. [PDF]

    [CVPR'18]AttnGAN: Fine-Grained Text to Image Generation with Attentional Generative Adversarial Networks.
    Tao Xu, Pengchuan Zhang, Qiuyuan Huang, Han Zhang, Zhe Gan, Xiaolei Huang, and Xiaodong He

    CVPR, 2018. [PDF]

    [T-PAMI'18] StackGAN++: Realistic Image Synthesis with Stacked Generative Adversarial Networks.
    Han Zhang*, Tao Xu*, Hongsheng Li, Shaoting Zhang, Xiaogang Wang, Xiaolei Huang, and Dimitris Metaxas.

    IEEE Transactions on Pattern Analysis and Machine Intelligence (T-PAMI), 2018. [PDF][Project]

    [Neuroinformatics'18] SegAN: Adversarial Network with Multi-scale L1 Loss for Medical Image Segmentation
    Yuan Xue*, Tao Xu*, Han Zhang, Rodney Long, and Xiaolei Huang

    Neuroinformatics, 2018. [PDF]

    [ICCV'17] StackGAN: Text to Photo-realistic Image Synthesis with Stacked Generative Adversarial Networks
    Han Zhang, Tao Xu, Hongsheng Li, Shaoting Zhang, Xiaolei Huang, Xiaogang Wang, and Dimitris Metaxas.
    ICCV, 2017. Oral Presentation [arXiv][PDF] [Project]

    [CVPR'17] Link the head to the "peak'': Zero Shot Learning from Noisy Text descriptions at Part Precision
    Mohamed Elhoseiny*, Yizhe Zhu*, Han Zhang, Ahmed Elgammal
    CVPR 2017. [PDF]

    [CVPR'16] SPDA-CNN: Unifying Semantic Part Detection and Abstraction forFine-grained Recognition
    Han Zhang*, Tao Xu*, Mohamed Elhoseiny, Xiaolei Huang, Shaoting Zhang, AhmedElgammal, and Dimitris Metaxas
    CVPR 2016. [PDF]

    [MICCAI'16] Multimodal Deep Learning for Cervical Dysplasia Diagnosis
    Tao Xu*, Han Zhang*, Xiaolei Huang, Shaoting Zhang, and Dimitris Metaxas.
    MICCAI 2016 (Early acceptance rate, 10%). [PDF]

    [PR'16] Multi-feature based Benchmark for Cervical Dysplasia Classification Evaluation
    Tao Xu, Han Zhang, Cheng Xin, Edward Kim, L Rodney Long, Zhiyun Xue, Sameer Antani, and Xiaolei Huang.
    Pattern Recognition 2016. [PDF]

    [ISBI'14] Robust shape prior modeling based on Gaussian-Bernoulli RestrictedBoltzmann Machine
    Han Zhang, Shaoting Zhang, Kang Li and Dimitris Metaxas.
    IEEE International Symposium on Biomedical Imaging, 2014. [PDF ]
    Oral presentation 

    TA Work
    [Fall 17]                   CS580: Topics in Computers in Biomedicine
    [Spring16]               CS536: Machine Learning
    Spring16]               CS520: Introduction to Artificial Intelligence
    [Spring15]               CS213: Software Methodology
    [Spring13,14]          CS111: Introduction to Computer Science
    [Fall12,13,14,15]    CS334: Introduction to Imaging and Multimedia