Assistant Professor
Department of Electrical Engineering and Computer Science

	South Dakota State University Daktronics Eng. Hall 123 Brookings, SD 57007
	(605) 688-6304
	Kaiqun.Fu@sdstate.edu
	Google Scholar

Dr. Kaiqun Fu is an Assistant Professor in Department of Electrical Engineering and Computer Science (EECS), South Dakota State University (SDSU), Brookings, SD. He received his Ph.D. degree from the Virginia Tech, in 2021, and the M. S. degree from Virginia Tech in 2016. He worked on research projects involving urban perception with deep learning, traffic impact analysis for smart cities, and emerging technologies prediction. His research and teaching focus on Spatial Data Mining, Machine Learning, Deep Learning, GeoAI, Social Media Analysis, and Urban Computing.

Before that, he holds a Bachelor of Science in Electrical Engineering in the direction of Electronic Communication Engineering.

Education

• Ph.D. in Computer Science, 2021
  Virginia Tech
• M.S. in Computer Science, 2016
  Virginia Tech

Recent Pulication

• AAAI Blocking Influence at Collective Level with Hard Constraints
  Zonghan Zhang, Subhodip Biswas, Fanglan Chen, Kaiqun Fu, Taoran Ji, Chang-Tien Lu, Naren Ramakrishnan and Zhiqian Chen
  Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22) (2022).
  Paper | Slides
• AAAI Augmentation of Chinese Character Representations with Compositional Graph Learning
  Jason Wang, Kaiqun Fu, Zhiqian Chen and Chang-Tien Lu
  Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22) (2022).
  Paper | Slides
• AAAI Early Forecast of Traffic Accident Impact based on a Single-Snapshot Observation
  Guangyu Meng, Qisheng Jiang, Kaiqun Fu, Beiyu Lin, Chang-Tien Lu and Zhqian Chen
  Proceedings of the Thirty-Sixth AAAI Conference on Artificial Intelligence (AAAI-22) (2022).
  Paper | Slides
• IEEE BigData HastGCN: A Hierarchical Attention-Based Spatiotemporal Graph Convolutional Network for Traffic Incident Impact Forecasting
  Kaiqun Fu, Taoran Ji, Nathan Self, Zhiqian Chen, and Chang-Tien Lu
  Proceedings of the EEE International Conference on Big Data (2021).
  (2021).
  Paper | Slides
• AAAI Dynamic Multi-Context Attention Networks for Citation Forecasting of Scientific Publications
  Taoran Ji, Nathan Self, Kaiqun Fu, Zhqian Chen, Naren Ramakrishnan, and Chang-Tien Lu
  Proceedings of the Thirty-Fifth AAAI Conference on Artificial Intelligence (AAAI-21) (2021).
  Paper | Slides
• IEEE BigData SOSNet: A Graph Convolutional Network Approach to Fine-Grained Cyberbullying Detection
  Jason Wang, Kaiqun Fu, and Chang-Tien Lu
  Proceedings of the EEE International Conference on Big Data (2020).
  Paper | Slides
• ASONAM RISECURE: Metro Security Incidents And Threat DetectionUsing Social Media
  Omer Zulfiqar, Yi-Chun Chang, Po-Han Chen, Kaiqun Fu, and Chang-Tien Lu Proceedings of the IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining, ASONAM (2020).
  Paper | Slides

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Full List of Papers