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Hao Zhang

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

Email: hao AT cs.cmu.edu



Bio

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

My research interest is majorly in scalable machine learning, deep learning, and large-scale ML applications in computer vision and natural language processing. I co-design models, algorithms and systems to scale out ML to larger data, problems and applications, to ease the prototyping of complex ML models and algorithms, and to automate the distribution of ML programs.

Several of my works including Poseidon, Cavs, and GeePS, are parts of the Petuum project, and now being commericialized at the Pittsburgh-based startup Petuum Inc.

News: My most recent project is Arion. Checkout my recent EXPO talk at ICML'19.

Please check out my CV for latest information.

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)
ICLR 2019
Toward Understanding the Impact of Staleness in Distributed Machine Learning
Wei Dai, Yi Zhou, Nanqing Dong, Hao Zhang, Eric P. Xing
ICLR 2019
Symbolic Graph Reasoning Meets Convolutions
NIPS 2018
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

Software

I have built or contributed 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 - 2018.4
  • Research Intern, Microsoft Research Asia, 2013 - 2014
  • Software Engineer Intern, Microsoft Shanghai, 2011 - 2012

Teaching