
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
-
Grants
Time series multi-modal foundation model for near-real-time land surface dynamics
characterization in support of ESDT,
Role: Co-PI
Sponsored by: National Aeronautics & Space Administration (NASA), PI – Hankui Zhang, Co-PI - Xiaoyang Zhang; Grant No. 23-AIST23-0106, Jun. 1, 2025 to May 31, 2027
Amount: $462,516
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
Full List of Papers