Assistant Professor
Director of kAI Lab
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, from Dr. Chang-Tien Lu's Group. 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!

News

• Paper Our paper Location Representation Learning For Urban Management with Heterogeneous Graph is accepted at MDM 2025.
  
• Service Kaiqun will serve as the Demo Co-Chair for ICDM 2025. We invite demo contributions to the IEEE ICDM 2025. See you in Washington DC this Fall!
  
• Grant One grant awarded by National Aeronautics & Space Administration (NASA), Jun. 1, 2025 to May 31, 2027
  Role: Co-PI
  Project Name: Time series multi-modal foundation model for near-real-time land surface dynamics characterization in support of ESDT,
  Amount: $462,516
• Paper Our paper A Large Language Model-based Fake News Detection Framework with RAG Fact-Checking is accepted at IEEE BigData 2024.
  

Recent Pulication

• MDM Location Representation Learning For Urban Management with Heterogeneous Graph
  Xiaozhu Jin, Xianding Tao, Kaiqun Fu
  Proceedings of The 26th IEEE International Conference on Mobile Data Management (2025).
  Paper | Slides
• IEEE BigData A Large Language Model-based Fake News Detection Framework with RAG Fact-Checking
  Yangxiao Bai, Kaiqun Fu
  Proceedings of the IEEE International Conference on Big Data (2024).
  Paper | Slides
• NAPS A Real-Time Digital Simulator Accelerated Reinforcement Learning Training Environment for Power System Frequency Dynamics
  Pooja Aslami, Tara Aryal, Niranjan Bhujel, Hossein Moradi Rekabdarkolaee, Kaiqun Fu, Zongjie Wang, Timothy M Hansen
  2024 North American Power Symposium (NAPS) (2024).
  Paper | Slides
• 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

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Full List of Papers