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 models

  • Time Series Forecasting


  • 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



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

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

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

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

Workshops or Preprints

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

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

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

Work Experience

  • 2016.6 - 2016.8 : Research Intern, Visa Research

  • 2015.2 - 2015.7 : Software Engineer, December & Company

  • 2014.5 - 2014.12 : Research Assistant, University of Michigan