Sungyong Seo

sungyons at usc dot edu

About Me

My name is Sungyong Seo (CV) and I am a Ph.D. student in the Computer Science Department of University of Southern California. I work on Machine Learning under supervision of Prof. Yan Liu.

I am interested in machine learning problems in general. More specifically, my research mainly focuses on principled techniques for extracting knowledge from the complex structures or networks and using it to build predictive models – in that way merging insights both from data mining and machine learning. Furthermore, combining the structural knowledge with temporal behaviors is the topic which I am interested in. I am working on the following topics:

  • Spatiotemporal data mining

  • Graph-based deep learning

  • Time Series Forecasting

Education

  • 2015 - Present : PhD, Computer Science, University of Southern California

  • 2012 - 2014 : MS, Electrical Engineering, University of Michigan

  • 2005 - 2012 : BS, Electrical Engineering, Seoul National University

Publications

Conferences

  • Social Bots for Online Public Health Interventions
    Ashok Deb, Anuja Majmundar, Sungyong Seo, Akira Matsui, Rajat Tandon, Shen Yan, Jon-Patrick Allem and Emilio Ferrara
    IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) 2018.
    [Paper]

  • Automatically Inferring Data Quality for Spatiotemporal Forecasting
    Sungyong Seo, Arash Mohegh, George Ban-Weiss, and Yan Liu.
    6th International Conference on Learning Representations (ICLR) 2018.
    [Paper]

  • Partially Generative Neural Networks for Gang Crime Classification with Partial Information
    Sungyong Seo, Hau Chan, P. Jeffrey Brantingham, Jorja Leap, Phebe Vayanos, Milind Tambe and Yan Liu.
    AAAI/ACM Conference on AI, Ethics, and Society 2018. (Oral presentation)
    [Paper]

  • CSI: A Hybrid Deep Model for Fake News Detection
    Sungyong Seo*, Natali Ruchansky*, and Yan Liu.
    Proceedings of the 26th ACM International on Conference on Information and Knowledge Management (CIKM) 2017.
    [Paper]

  • Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction
    Sungyong Seo, Jing Huang, Hao Yang, and Yan Liu.
    Proceedings of the 11th ACM Conference on Recommender Systems (RecSys) 2017.
    [Paper]

Workshops or Preprints

  • Data Quality Network for Spatiotemporal Forecasting
    Sungyong Seo, Arash Mohegh, George Ban-Weiss, and Yan Liu.
    Deep Learning for Physical Sciences Workshop at the 31st Conference on Neural Information Processing Systems (DLPS-NIPS) 2017.
    [Paper]

  • Graph Convolutional Autoencoder with Recurrent Neural Networks for Spatiotemporal Forecasting
    Sungyong Seo, Arash Mohegh, George Ban-Weiss, and Yan Liu.
    Proceedings of the Seventh International Workshop on Climate Informatics 2017.
    [Paper]

  • Representation Learning of Users and Items for Review Rating Prediction Using Attention-based Convolutional Neural Network
    Sungyong Seo, Jing Huang, Hao Yang, and Yan Liu.
    3rd International Workshop on Machine Learning Methods for Recommender Systems (MLRec)(SDM’17) 2017.
    [Paper]

Work Experience