Dihong Gong
University of Florida
Data Science Research Lab
Department of Computer Science
412 Newell Drive, Room 457
Gainesville, FL 32611
Email: gongd (at) ufl (dot) edu

I am a fifth-year Ph.D. candidate in Computer Science, University of Florida. My research interests lie in the areas of machine learning, computer vision, information extraction, and data mining. I am currently working on multimodal knowledge extraction from the web, advised by Dr. Daisy Zhe Wang.

My current research objective is to enable multimodal analysis for large-scale knowledge extraction from the web. The multimodal analysis combines complementary information clues across different modalities, which can be beneficial for enhanced extraction accuracy. In the meanwhile, I develop scalable parallel processing systems using Hadoop HDFS, Apache Spark, and Cuda deep learning, to enable efficient information extraction for big data.

Peer Reviews


  1. Multimodal Knowledge Extraction
    Dihong Gong, Daisy Zhe Wang (supervisor)
    Primary Ph.D. research topic.
    May 2015 - Present

    We consider the problem of extracting instances of both text categories (e.g. persons, cities) and image categories (e.g. vehicles) from an open domain. Our goal is to design efficient and scalable algorithms to utilize text and image information and extract knowledge from the multimodal web contents. Such algorithms include: multimodal graphical models, deep learning fusion models, multimodal rules, and sparse logistic regression models based on Skip-Gram models for word-to-vec embeddings. Extensive experimental evaluations based on large-scale real-world web knowledge mining shows that our approach can achieve good improvement over the state-of-the-art algorithms. More info: Blog1.

  2.  Computer Vision
    Dihong Gong, Zhifeng Li (supervisor)
    Research Assistant at Shenzhen Institutes of Advanced Technology, Chiese Academy of Science
    Aug 2011 - Dec 2016

    Research topics include face recognition (cross-modality, age-invariant, video-based), and image-based human age estimation. It involves the development and application of many important machine learning concepts including: adaptive feature extraction, subspace learning, kernel methods, ensemble methods, Gaussian Process, variational inference, graphical models, deep learning, etc.

  3.  Google Internship (Mountain View)
    Dihong Gong, Na Tang (supervisor)
    Tech Intern at Google Search
    May 2016 - Aug 2016

    Design and Implement a Parallel DocChart model to allow document annotators run in parallel within Goldmine system.

  4. NIST NEON Datascience Evaluation
    Jan 2017 - Present

    This project aims at developing a NIST data science contest for plant identification with Neon remote sensing data. In this project, I am responsible for defining the evaluation metrics as well as baseline algorithms for three different tasks, and coordinating implementation of baseline algorithms and development of automatic evaluation system. More info: Website, Github.

  5. Deep Neural Networks OpenCL Implementations
    Dihong Gong, Siva Prasad and Siliang Xia
    Work is released under BSD license.
    Sep 2014 - Nov 2015

    The objective of this project is to develop a device independent visual designer for deep learning neural networks -- You design your networks with our GUI tools, and we generate codes for you to run on a wide range of devices including GPU and CPU from different vendors (e.g. Intel, AMD and Nvidia). To allow device-independent implementation, we write the system with C++ and OpenCL languages. Our system is designed to support a wide range of deep learning algorithms such as convolutional neural networks, recurrent neural networks, deep Boltzmann machines, etc. With scalability in mind, our codes are carefully designed for optimized performance.

  6. NIST Pre-Pilot Datascience Evaluation
    Dihong Gong, Daisy Zhe Wang
    Sep 2015 - Jan 2016

    We participated in the Pre-pilot data science evaluation organized by the National Institute of Standards and Technology (NIST), the primary goal of which is to develop and exercise the evaluation process in the context of data science. The evaluation consists of four tasks including data cleaning, data alignment, forecasting and prediction. Our DSR lab has participated the data cleaning and traffic event prediction tasks, and submitted several running systems (most of which are based on student projects in our data science class) of different algorithms and configurations. More info: Blog1.

  7. Biological Networks Learning
    Dihong Gong, Ahmet Ay, Tamer Kahveci (supervisor)
    Research Assistant at Bioinformatics Lab, University of Florida
    Mar 2014 - Sep 2014

    We proposed network-based classification model for cancer prediction using gene expression, and compare our model to other classification models such as support vector machines, naive bayes classifier, k nearest neighbors, C4.5 decision trees, random forest, etc. Additionally, we also proposed an efficient algorithm (D-Hiden) to find the hierarchy of the genes in dynamically evolving gene regulatory network topologies. I was reponsible for the implementation of all the algorithms, and conducting comparison evaluation.

  8. TTCN-3 Compiler Design and Implementation
    Dihong Gong, Sihai Zhang
    Undergraduate student at University of Science and Technology of China
    Oct 2009 - Feb 2011

    We designed an efficient compiler implementation framework for TTCN-3 programming language, and implemented TTCN-3 compiler that compiled TTCN-3 source codes into ASM codes. This work is from National innovation experiment program for college students of China, supported by National Ministry of Education of China with Grant 091035837.


  1. "Multimodal Learning for Web Information Extraction",
    D. Gong, D. Z. Wang, Y. Peng
    ACM Multimedia (Full Research Paper), 2017. Highest ranked in conference!

  2. "Extracting Visual Knowledge from the Web with Multimodal Learning",
    D. Gong, D. Z. Wang
    International Joint Conference on Artificial Intelligence (IJCAI), 2017. Highest ranked in conference!

  3. "Multi-feature Anisotropic Orthogonal Gaussian Process for Automatic Age Estimation",
    Z. Li, D. Gong, K. Zhu, D. Tao, X. Li
    ACM Transactions on Intelligent Systems and Technology, 2017. (Impact Factor: 3.19)

  4. "Heterogeneous Face Recognition: A Common Encoding Feature Discriminant Approach",
    D. Gong, Z. Li, W. Huang, X. Li, D. Tao
    IEEE Transactions on Image Processing, 2017. (Impact Factor: 4.83)

  5. "Aging Face Recognition: A Hierarchical Learning Model Based on Local Patterns Selection",
    Z. Li, D. Gong, X. Li, D. Tao
    IEEE Transactions on Image Processing, 2016. (Impact Factor: 4.83)

  6. "Multimodal Ensemble Fusion for Disambiguation and Retrieval",
    Y. Peng, X. Zhou, D. Wang, I. Patwa, D. Gong, C. Fang
    IEEE Multimedia, 2016. (Impact Factor: 2.85)

  7. "Mutual Component Analysis for Heterogeneous Face Recognition",
    Z. Li, D. Gong, Q. Li, D. Tao, X. Li
    ACM Transactions on Intelligent Systems and Technology, 2015. (Impact Factor: 3.19)

  8. "A maximum Entropy Feature Descriptor for Age Invariant Face Recognition",
    D. Gong, Z. Li, D. Tao, J. Liu, X. Li
    Computer Vision and Pattern Recognition, 2015. Highest ranked in conference!

  9. "Probabilistic Ensemble Fusion for Multimodal Word Sense Disambiguation",
    Y. Peng, D. Z. Wang, I. Patwa, D. Gong
    IEEE International Symposium on Multimedia, 2015.

  10. "Learning Compact Feature Descriptor and Adaptive Matching Framework for Face Recognition",
    Z. Li, D. Gong, X. Li, D. Tao
    IEEE Transactions on Image Processing, 2015. (Impact Factor: 4.83)

  11. "Hierarchical Decomposition of Dynamically Evolving Regulatory Networks",
    A. Ay*, D. Gong*, T. Kahveci * Equal Contributors
    Journal of BMC Bioinformatics, 2015. (Impact Factor: 2.58)

  12. "Network-based Prediction of Cancer under Genetic Storm",
    A. Ay*, D. Gong*, T. Kahveci * Equal Contributors
    Cancer Informatics, 2015. (Impact Factor: 1.62)

  13. "Orthogonal Gaussian Process for Large-Scale Automatic Age Estimation",
    K. Zhu D. Gong, Z. Li, X. Tang
    ACM Multimedia (Short Research Paper), 2014. Highest ranked in conference!

  14. "Multi-feature subspace analysis for audio-video based multi-modal person recognition",
    D. Gong, N. Li, Z. Li, Y. Qiao
    IEEE International Conference on Information Science and Technology, 2014.

  15. " Common Feature Discriminant Analysis for Matching Infrared Face Images to Optical Face Images",
    Z. Li D. Gong, Y. Qiao, D. Tao
    IEEE Transactions on Image Processing, 2014. (Impact Factor: 4.83)

  16. "Hidden Factor Analysis for Age Invariant Face Recognition",
    D. Gong, Z. Li, D. Lin, J. Liu, X. Tang
    IEEE International Conference on Computer Vision, 2013. Highest ranked in conference!

  17. "Multi-feature Canonical Correlation Analysis for Face Photo-Sketch Image Retrieval",
    D. Gong, Z. Li, J. Liu, Y. Qiao
    ACM Multimedia (Short Research Paper), 2013. Highest ranked in conference!

  18. "Maximum Correlation Feature Descriptor for Heterogeneous Face Recognition",
    D. Gong, J.Y. Zheng
    IEEE Asian Conference on Pattern Recognition, 2013. Best Student Paper Nomination!

  19. "Semantic Model for Video based Face Recognition",
    D. Gong, K. Zhu, Z. Li, Y. Qiao
    IEEE International Conference on Information and Automation, 2013.

  20. "TTCN-3 Test Architecture based on Port-Oriented Design and Assembly Language Implementation",
    D. Gong, S. Zhang
    ASME International Conference on Computer Application and System Modeling, 2012.

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