About Me

My name is Sungyong Seo and I am a Software Engineer at Google Cloud AI. Prior to joining Google, I was a Ph.D. student in the Computer Science Department of University of Southern California under the supervision of Professor Yan Liu. I also spent time at the NYU Center for Data Science to collaborate with Professor Kyunghyun Cho.

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:

• Physics-inspired machine learning
• Spatiotemporal data mining
• Graph-based neural networks
• Recommendation systems

Publications

Conferences

• Controlling Neural Networks with Rule Representations
  Sungyong Seo, Sercan O Arik, Jinsung Yoon, Xiang Zhang, Kihyuk Sohn, Tomas Pfister
  Neural Information Processing Systems (NeurIPS) 2021.
  [arXiv Paper] [Code] [K1st World Symposium]
• Physics-aware Spatiotemporal Modules with Auxiliary Tasks for Meta-Learning
  Sungyong Seo, Chuizheng Meng, Sirisha Rambhatla, Yan Liu
  International Joint Conference on Artificial Intelligence (IJCAI) 2021.
  NeurIPS Workshop Machine Learning and the Physical Sciences (ML4PS) 2020.
  [Paper] [Workshop Paper] [Workshop Poster]
• Physics-aware Difference Graph Networks for Sparsely-Observed Dynamics
  Sungyong Seo*, Chuizheng Meng*, Yan Liu
  International Conference on Learning Representations (ICLR) 2020.
  [Paper] [OpenReview] [Code] [Bibtex]
• A Deep Structural Model for Analyzing Correlated Multivariate Time Series
  Changwei Hu, Yifan Hu, Sungyong Seo
  IEEE International Conference on Machine Learning and Applications (ICMLA) 2019.
  [Paper] [Bibtex]
• Social Bots for Online Public Health Interventions
  Ashok Deb, Anuja Majmundar, Sungyong Seo, Akira Matsui, Rajat Tandon, Shen Yan, Jon-Patrick Allem, Emilio Ferrara
  IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (ASONAM) 2018.
  [Paper] [Bibtex]
• Automatically Inferring Data Quality for Spatiotemporal Forecasting
  Sungyong Seo, Arash Mohegh, George Ban-Weiss, Yan Liu
  International Conference on Learning Representations (ICLR) 2018.
  [Paper] [OpenReview] [Bibtex]
• Partially Generative Neural Networks for Gang Crime Classification with Partial Information
  Sungyong Seo, Hau Chan, P. Jeffrey Brantingham, Jorja Leap, Phebe Vayanos, Milind Tambe, Yan Liu
  AAAI/ACM Conference on AI, Ethics, and Society (AIES) 2018. (Oral presentation)
  [Paper] [Bibtex]
• CSI: A Hybrid Deep Model for Fake News Detection
  Sungyong Seo*, Natali Ruchansky*, Yan Liu
  ACM International Conference on Information and Knowledge Management (CIKM) 2017.
  [Paper] [Bibtex]
• Interpretable Convolutional Neural Networks with Dual Local and Global Attention for Review Rating Prediction
  Sungyong Seo, Jing Huang, Hao Yang, Yan Liu
  ACM Conference on Recommender Systems (RecSys) 2017.
  [Paper] [Bibtex]

Workshops or Preprints

• Coronavirus on social media: Analyzing misinformation in Twitter conversations
  Karishma Sharma, Sungyong Seo, Chuizheng Meng, Sirisha Rambhatla, Aastha Dua, Yan Liu
  [arXiv Paper][Project Page][Fortune Eye on AI]
• Contextual Understanding of Homicide Reports in Los Angeles County
  Sungyong Seo, Umang Gupta, Jiageng Zhu, P. Jeffrey Brantingham, Yan Liu
  SoCal NLP Symposium 2019.
  [Symposium page]
• Data-driven Temporal Attribution Discovery of Temperature Dynamics based on Attention Networks
  Sungyong Seo, Jiachen Zhang, George Ban-Weiss, Yan Liu
  International Workshop on Climate Informatics (CI) 2019.
  [Workshop page]
• Differentiable Physics-informed Graph Networks
  Sungyong Seo and Yan Liu
  ICLR Workshop on Representation Learning on Graphs and Manifolds (ICLR-RLGM) 2019.
  AAAI 2021 Spring Symposium on Combining Artificial Intelligence and Machine Learning with Physics Sciences (AAAI-MLPS) 2021.
  [arXiv Paper] [Workshop page] [Symposium Proceedings] [Code]
• Data Quality Network for Spatiotemporal Forecasting
  Sungyong Seo, Arash Mohegh, George Ban-Weiss, Yan Liu
  NeurIPS Workshop on Deep Learning for Physical Sciences (NeurIPS-DLPS) 2017.
  [Paper]
• Graph Convolutional Autoencoder with Recurrent Neural Networks for Spatiotemporal Forecasting
  Sungyong Seo, Arash Mohegh, George Ban-Weiss, Yan Liu
  International Workshop on Climate Informatics (CI) 2017.
  [Paper]
• Representation Learning of Users and Items for Review Rating Prediction Using Attention-based Convolutional Neural Network
  Sungyong Seo, Jing Huang, Hao Yang, Yan Liu
  SDM Workshop on Machine Learning Methods for Recommender Systems (SDM-MLRec) 2017.
  [Paper]