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 Exploration on Physics-Informed Neural Networks on Partial Differential Equations
  Hoa Ta, ShiWen Wong, Nathan McClanahan, Jung-Han Kimn and Kaiqun Fu
  Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-23) (2023).
  Paper | Slides
• AAAI PanTop: Pandemic Topic Detection and Monitoring System
  Yangxiao Bai and Kaiqun Fu
  Proceedings of the Thirty-Seventh AAAI Conference on Artificial Intelligence (AAAI-23) (2023).
  Paper | Slides
• SDM Early Forecasting Of The Impact Of Traffic Accidents Using A Single Shot Observation
  Guangyu Meng, Qisheng Jiang, Kaiqun Fu, Beiyu Lin, Chang-Tien Lu and Zhqian Chen
  Proceedings of the 2022 SIAM International Conference on Data Mining (SDM) (2022).
  Paper | Slides
• Journal Detecting Anomalous Traffic Behaviors With Seasonal Deep Kalman Filter Graph Convolutional Neural Networks
  Yanshen Sun, Yen-Cheng Lu, Kaiqun Fu, Fanglan Chen and Chang-Tien Lu
  Journal of King Saud University-Computer and Information Sciences (2022).
  Paper | Slides
• ICANN Multi-view Cascading Spatial-temporal Graph Neural Network For Traffic Flow Forecasting
  Zibo Liu, Kaiqun Fu and Xiaotong Liu
  International Conference on Artificial Neural Networks (2022).
  Paper | Slides
• Book Chapter Metro Transit Disruptions Detection Using Social Media Mining And Graph Convolution
  Omer Zulfiqar, Yi-Chun Chang, Po-Han Chen, Kaiqun Fu, Chang-Tien Lu, David Solnick and Yanlin Li
  International Conference on Artificial Neural Networks (2022).
  Paper | Slides
• ITSC Granger Causal Inference for Interpretable Traffic Prediction
  Lei Zhang, Kaiqun Fu, Taoran Ji and Chang-Tien Lu
  IEEE 25th International Conference on Intelligent Transportation Systems (ITSC) (2022).
  Paper | Slides

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Full List of Papers