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.

I am actively seeking passionate and self-motivated PhD students with expertise in areas like machine learning, applied mathematics, geospatial analytics, and intelligent transportation. We particularly value skills in spatial data mining, graph neural networks, and physics-informed machine learning, especially those applicable to sectors such as intelligent transportation, power systems, and social networks.

If you are eager to contribute to research and think you would be a good fit, please do not hesitate to contact me at kaiqun.fu@sdstate.edu and fill out the application form at this link.

We look forward to potentially having you on our team!

Education

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

Recent Pulication

• TKDD Citation Forecasting with Multi-Context Attention-Aided Dependency Modeling
  Taoran Ji, Nathan Self, Kaiqun Fu, Zhiqian Chen, Naren Ramakrishnan, Chang-Tien Lu
  ACM Transactions on Knowledge Discovery from Data (2024).
  Paper | Slides
• IEEE BigData RoadFormer: Road-Anchored Adversarial Dynamic Graph Transformer for Unlimited-Range Traffic Incident Impact Prediction
  Yanshen Sun, Kaiqun Fu, Chang-Tien Lu
  Proceedings of the IEEE International Conference on Big Data (2023).
  Paper | Slides
• IEEE BigData Stock Movement and Volatility Prediction from Tweets, Macroeconomic Factors and Historical Prices
  Shengkun Wang, YangXiao Bai, Taoran Ji, Kaiqun Fu, Linhan Wang, Chang-Tien Lu
  Proceedings of the IEEE International Conference on Big Data (2023).
  Paper | Slides
• ASONAM ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Prediction
  Shengkun Wang, YangXiao Bai, Kaiqun Fu, Linhan Wang, Chang-Tien Lu, Taoran Ji
  Proceedings of the 2023 IEEE/ACM International Conference on Advances in Social Networks Analysis and Mining (2023).
  Paper | Slides
• NAPS ALERTA-Net: A Temporal Distance-Aware Recurrent Networks for Stock Movement and Volatility Prediction
  Tara Aryal, Pooja Aslami, Niranjan Bhujel, Hossein Moradi Rekabdarkolaee, Kaiqun Fu, Timothy M Hansen
  2023 North American Power Symposium (NAPS) (2023).
  Paper | Slides
• 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