Short Biography

I am Postdoc at UT Arlington in Prof. Prof. Junzhou Huang's lab. I am passionate at creating new machine learning algorithms to solve challenges met in the development of bioinformatics and biomedicine as well as developing real Artificial Agents to play challeging games, such as Atari, Alphago, Poker etc. Before the Postdoc at UTA, I spent one year in the Department of Statistics at the University of Michigan, starting September 2015. Prior to the working experience, I received my Ph.D. in the National Laboratory of Pattern Recognition (NLPR) at the Institute of Automation, Chinese Academy of Sciences (CASIA), advised by Prof. Chunhong Pan , and Prof. Shiming Xiang and Dr. Ying Wang. You can find my CV here.

Research Interests: Deep Reinforcement Learning, Unsupervised Learning, Manifold Learning, Remote Sensing Image Analysis, Hyperspectal Image Unmixing


  • 04/2017 One paper on Actor-Critic Reinforcement Learning for Mobile Health Intervention is provided on ArXiv

  • 03/2017 One paper accepted by CVPR 2017


Peer-reviewed Journal Papers

  1. Guangliang Cheng, Feiyun Zhu, Shiming Xiang and Chunhong Pan. "Road Centerline Extraction via Semisupervised Segmentation and Multidirection Nonmaximum Suppression" , IEEE Transactions on Geoscience and Remote Sensing Letters, vol. 37, no. 1, pp. 191-211 (IEEE TGRSL), 2016.

  2. Guangliang Cheng, Feiyun Zhu, Shiming Xiang, Ying Wang and Chunhong Pan. "Accurate urban road centerline extraction from VHR imagery via multiscale segmentation and tensor voting" , Elsevier Neurocomputing, vol. 205, pp. 407-420, 2016.

  3. Haichang Li, Ying Wang, Shiming Xiang, Jiangyong Duan, Feiyun Zhu, Chunhong Pan. "A label propagation method using spatial-spectral consistency for hyperspectral image classification" , International Journal of Remote Sensing, vol. 37, no. 1, pp. 191-211, 2016.

  4. Ying Wang, Chunhong Pan, Shiming Xiang and Feiyun Zhu. "Robust Hyperspectral Unmixing with Correntropy based Metric" , IEEE Transactions on Image Processing (IEEE TIP), vol. 24, no. 11, pp. 4027-4040, 2015.

  5. Guangliang Cheng, Feiyun Zhu, Shiming Xiang, Ying Wang, Chunhong Pan. "Semisupervised Hyperspectral Image Classification via Discriminant Analysis and Robust Regression" , IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, vol. 9, no. 2, pp. 595-608, 2015.

  6. Feiyun Zhu, Ying Wang, Bin Fan, Shiming Xiang, Gaofeng Meng and Chunhong Pan. "Spectral Unmixing via Data-guided Sparsity" , IEEE Transactions on Image Processing (IEEE TIP), vol. 23, no. 12, pp. 5412-5427, 2014.

  7. Feiyun Zhu, Ying Wang, Shiming Xiang, Bin Fan and Chunhong Pan. "Structured Sparse NMF for Hyperspectral Unmixing" , ISPRS Journal of Photogrammetry and Remote Sensing, vol. 88, no. 1, pp. 101-118, 2014.

Peer-reviewed Conference Papers

  1. Xinliang Zhu, Jiawen Yao, Feiyun Zhu and Junzhou Huang. "WSISA: Making Survival Prediction from Whole Slide Pathology Images" , International Conference on Computer Vision and Pattern Recognition (CVPR), 2017.

  2. Xiaoping Hu, Ying Wang, Feiyun Zhu and Chunhong Pan. "Learning-based fully 3D face reconstruction from a single image" , IEEE Conference on Acoustics, Speech and Signal Processing (ICASSP), pp. 1651-1655, 2016.

  3. Feiyun Zhu, Bin Fan, Xinliang Zhu, Ying Wang, Shiming Xiang and Chunhong Pan. "10,000+ Times Accelerated Robust Subset Selection (ARSS)" , Proc. Association for the Advancement of Artificial Intelligence (AAAI), 2015. (AR: 531/1991~26.67%).

  4. Guangliang Cheng, Ying Wang, Feiyun Zhu and Chunhong Pan. "Road extraction via adaptive graph cuts with multiple features" , IEEE Conference on Image Processing, pp. 3962-3966, 2015.

  5. Guangliang Cheng, Ying Wang, Yongchao Gong, Feiyun Zhu and Chunhong Pan. "Urban road extraction via graph cuts based probability propagation" , IEEE Conference on Image Processing, pp. 5072-5076, 2014.


  1. Feiyun Zhu, Peng Liao, Xinliang Zhu, Jiawen Yao and Junzhou Huang. "Cohesion-based Online Actor-Critic Reinforcement Learning for mHealth Intervention" , arXiv:1703.10039, 2017 (under review).

  2. Feiyun Zhu, Ying Wang, Bin Fan, Gaofeng Meng and Chunhong Pan. "Effective Spectral Unmixing via Robust Representation and Learning-based Sparsity" , arXiv:1409.0685, 2014 (under review).

Research Experiences

Learning Fine-grained Information from Weak Labels

2016.09 - Present
SMILE Lab, UT Arlington
In big data era, many data are with weak labels. One challenge is how to learn fine-grained information from those weak labels. In our problem of predicting patients' survival, only the giga-pixel level whole slide pathological images and patients-level survival labels are given. Some regions in the whole slide pathological images are discriminative for survival prediction and others are not. How to learn those fine-grained discriminative regions without expert-annotated ROIs is very challenging and critical in this problem. To solve this:
  • One deep attention-based model was proposed to solve the challenging problem. Very promising results were achieved on two types of cancers datasets;
  • We are trying to generalize our model to solve other fine-grained learning problems with weak labels.
1 paper accepted by CVPR 2017

Survival Analysis from Small Sample Medical Datasets

2016.03 - Present
SMILE Lab, UT Arlington
Survival analysis is very important in medical treatment, but leading research is challenged by three properties of medical data: 1) the datasets are usually in multiple views; 2) they are in small sample size; and 3) the whole slide pathology images are in gigapixel size. To solve those problems:
  • Efficient deep learning models with specific training strategies were developed to solve the first two problems;
  • A novel framework was also created to solve the third challenge.
1 paper accepted by IEEE BIBM 2016, 1 paper submitted to SIGKDD 2017 and 1 paper submitted to IJCAI 2017.

Big Image-Omics Data Analytics for Clinical Outcome Prediction

2015.09 - 2016.03
SMILE Lab, UT Arlington
In this research, we explored analyzing Image-Omics data for clinical outcome prediction from three aspects:
  • Integrating features extracted from pathology image patches with genetic signature expressions to improve the survival prediction accuracy;
  • Developing methods for imaging biomarker discovery;
  • Mapping clinical outcome correlated Imaging-Genetic data by developing supervised conditional Gaussian graphical model (SuperCGGM).
3 papers accepted by IEEE ISBI 2016, MICCAI 2016 and IEEE BIBM 2016 respectively.

Pedestrian Detection with RGBD Data in Outdoor Scene

2014.03 - 2015.06
Institute of Automation, Chinese Academy of Sciences
As a popular research topic, pedestrian detection has played an important role in both Intelligent Transportation Systems and Autonomous Vehicles. RGBD images are with the advantages of both RGB images and depth images. However, for the outdoor usage, the existing RGBD sensors like Kinect were inapplicable. Thus, in this research:
  • Created a novel method for RGB and depth images registration in outdoor scenes;
  • Collected over 10,000 RGBD outdoor pedestrain RGBD images;
  • Developed a fast pedestrian detection framework based on RGBD images.
1 patent on RGB image and depth image registration is pending.


Large Scale Learning for Complex Image-Omics Data Analytics

This project aims to develop computational tools for analyzing complex pathology image data as well genomics data. To solve the key and challenging problems in mining comprehensive heterogeneous image and genomic data, novel large scale learning tools and explore ways to integrate features from multiple data sources for clinical outcome prediction are developed. It will greatly support the Precision Medicine Initiative, which enables physicians to select individualized treatments. I work on developing feature learning from gigapixel whole slide pathology images and integrating imaging-omics data methods.

Miniature Autonomous Vehicle

The miniature autonomous vehicle was built from a toy car. It mainly consisted of a toy car, 3 web cameras, 1 ultrasonic radar and a motherboard equipped with Ubuntu 12.04LTS. It could do traffic signs recognition, traffic light recognition, road line detection and also be with the basic functions of a toy car. I was in charge of building the traffic signs recognition module and testing the hardware & software architecture.

SOAR-based Air Traffic Control Simulation System

Using SOAR as a cognition architecture to simulate an air traffic controller’s decision making could potentially make the training of an air traffic controller easier and with more fun. I was the software architect of the whole system and the main designer of the rules used in SOAR architecture

Skills & Proficiency



Theano & Lasagne


Linux & Vim


Bash Scripting