Kaiqun Fu

Dr. Kaiqun Fu is an Assistant Professor in the McComish Department of Electrical Engineering and Computer Science at South Dakota State University (SDSU). He holds a Ph.D. and M.S. in Computer Science from Virginia Tech (2021 and 2016). His research focuses on spatial data mining, spatiotemporal event analysis, graph neural networks, and urban computing applications such as traffic impact prediction and social media-driven insights. He also explores physics-informed machine learning for power systems and interdisciplinary topics like 'deaths of despair' in rural areas.

Education:

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

Research Interests:

His work emphasizes machine learning and deep learning applications in spatial-temporal domains, including:

• Graph neural networks for traffic incident prediction
• Social media analysis for urban challenges
• Physics-informed models for power grid stability
• Citation forecasting in scientific publications

Grants & Projects:

• NSF CRII ($174,734): Spatiotemporal impacts of traffic events via graph neural networks (2024–2026)
• NSF EAGER ($300,000): Socio-economic impacts of emerging technologies (2024–2026)
• SDSU RSCA ($10,118): Graph transformer-based location learning (2023–2024)

Professional Involvement:

He chairs ACM SIGSPATIAL's SRC committee, serves on SDSU's Computer Science curriculum committees, and is an IEEE member. He co-edits Frontiers in Big Data and advises on interdisciplinary projects like climate-impacted grid security (NSF RII Track-2, $750,000).

Labs/Teams:

Collaborates with interdisciplinary groups focusing on smart cities, data-driven infrastructure resilience, and GeoAI applications.