Hao Zhang

Ph.D. Student
The Robotics Institute
School of Computer Science
Carnegie Mellon University
Pittsburgh, PA 15213, USA

Email: hao AT


I am currently a Ph.D. student in the Robotics Institute, Carnegie Mellon University. My advisor is Prof. Eric Xing.

My research interest is in scalable and structured machine learning, deep learning, and their applications in computer vision and natural lauguage processing. I (co-)design models, algorithms and systems to enable machine learning to be applied/delopyed on larger-scale problems and applications.

Please check out my CV for latest information.


  • I am currently the Director of Scalable ML at Petuum Inc., a startup that's building an enterprise software development platform to serve the full spectrum of artificial intelligence and machine learning applications. We are hiring! Drop me an email if you are intereted in joining us!
  • Publications

    AutoLoss: Learning Discrete Schedules for Alternate Optimization
    Hao Zhang*, Haowen Xu*, Zhiting Hu, Xiaodan Liang, Ruslan Salakhutdinov, and Eric P. Xing (* indicates equal contributions)
    arXiv preprint, 2018
    Toward Understanding the Impact of Staleness in Distributed Machine Learning
    Wei Dai, Yi Zhou, Nanqing Dong, Hao Zhang, Eric P. Xing
    arXiv preprint, 2018
    Symbolic Graph Reasoning Meets Convolutions
    NIPS 2018
      Coming soon
    Cavs: A Vertex-centric Programming Interface for Dynamic Neural Networks
    Hao Zhang*, Shizhen Xu*, Graham Neubig, Wei Dai, Qirong Ho, Guangwen Yang, and Eric P. Xing (* indicates equal contributions)
    ATC 2018 (Oral), AISys@SOSP'17, MLSys@NIPS'17
    Generative Semantic Manipulation with Contrasting GAN
    ECCV 2018
    Structured Generative Adversarial Networks
    Hao Zhang*, Zhijie Deng*, Xiaodan Liang, Luona Yang, Shizhen Xu, Jun Zhu, and Eric P. Xing (* indicates equal contributions)
    NIPS 2017(Nvidia Pioneer Research Award!)
    Poseidon: An Efficient Communication Architecture for Distributed Deep Learning on GPU Clusters
    ATC 2017 (Oral)
    Recurrent Topic-Transition GAN for Visual Paragraph Generation
    Xiaodan Liang, Zhiting Hu, Hao Zhang, Chuang Gan, and Eric P. Xing
    ICCV 2017
    SCAN: Structure Correcting Adversarial Network for Chest X-rays Organ Segmentation
    Wei Dai, Joseph Doyle, Xiaodan Liang, Hao Zhang, Nanqing Dong, Yuan Li, and Eric P. Xing
    arXiv preprint, 2017
    ZM-Net: Real-time Zero-shot Image Manipulation Network
    arXiv preprint, 2017
    Poseidon: A System Architecture for Efficient GPU-based Deep Learning on Multiple Machines
    ATC 2016 (Poster), MLSys Workeshop@ICML 2016 (Spotlight)
    Learning Concept Taxonomies from Multi-modal Data
    Hao Zhang, Zhiting Hu, Yuntian Deng, Mrinmaya Sachan, Zhicheng Yan, and Eric P. Xing
    ACL 2016 (Oral)
    GeePS: Scalable Deep Learning on Distributed GPUs with a GPU-specialized Parameter Server
    EuroSys 2016
    Combining the Best of Convolutional Layers and Recurrent Layers: A Hybrid Network for Semantic Segmentation
    Zhicheng Yan, Hao Zhang, Yangqing Jia, Thomas Breuel, Yizhou Yu
    arXiv preprint, 2016
    Automatic Photo Adjustment Using Deep Learning
    TOG Vol.35 No.2, ICCP 2016 (Invited Poster)
    On the Reducibility of Submodular Functions
    Jincheng Mei, Hao Zhang, and Baoliang Lu
    AISTATS 2016
    HD-CNN: Hierarchical Deep Convolutional Neural Network for Large Scale Visual Recognition
    Zhicheng Yan, Hao Zhang, Robinson Piramuthu, Vignesh Jagadeesh, Dennis DeCoste, Wei Di, and Yizhou Yu
    ICCV 2015
    Dynamic Topic Modeling for Monitoring Market Competition from Online Text and Image Data
    Hao Zhang, Gunhee Kim, and Eric P. Xing
    KDD 2015 (Oral)
    A Boosting-based Spatial-Spectral Model for Stroke Patients' EEG Analysis in Rehabilitation Training
    Ye Liu*, Hao Zhang*, and Liqing Zhang
    (* indicates equal contribution)
    ECAI 2014, IEEE TNSRE 2015
    Gaussian Mixture Modeling in Stroke Patients' Rehabilitation EEG Data Analysis
    Hao Zhang, Ye Liu, Jianyi Liang, Jianting Cao, and Liqing Zhang
    EMBC 2013
    A Tensor-Based Scheme for Stroke Patients' Motor Imagery EEG Analysis in BCI-FES Rehabilitation Training
    Ye Liu, Mingfen Li, Hao Zhang, Junhua Li, Jie Jia, Yi, Wu, Jianting Cao, and Liqing Zhang
    EMBC 2013, JNM 2013


    I build or contribute to many projects for large-scale machine learning, some of them are open sourced.

    Working Experience

    • Director of Scalable ML, Petuum Inc., 2018.4 - Now
    • Tech Lead and Research Scientist, Petuum Inc., 2016.10 - Now
    • Research Intern, Microsoft Research Asia, 2013 - 2014
    • Software Engineer Intern, Microsoft Shanghai, 2011 - 2012