Kaiqun Fu

Kaiqun Fu

Assistant Professor

Biography

Dr. Kaiqun Fu is an assistant professor in Department of Electrical Engineering and Computer Science (EECS), South 糖心视频 State University (SDSU), Brookings. He received his Ph.D. from the Virginia Tech, in 2021, and the M.S. 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.

Education

  • Ph.D. in computer science | Virginia Tech
  • M.S. in computer science | Virginia Tech

Academic and Professional Experience

Academic 糖心视频s
Academic 糖心视频s
  • Spatial Data Mining
  • Machine Learning
  • Natural Language Processing
Academic Responsibilities
  • CSC 792: Spatial Data Mining
  • CSC 492/592: Introduction to Machine Learning
  • CSC 705: Design and Analysis of Computer Algorithms
Committees and Professional Memberships

Committees:

  • External:
    • ACM SIGSPATIAL: SRC Chair (2021, 2022)
    • Guest Associate Editor for Frontiers in BigData
  • SDSU:
    • Computer Science Graduate Committee
    • Computer Science Graduate Curriculum Committee
    • Computer Science Undergrad Curriculum Committee
    • Search Committee (EECS, ABE)

Professional Memberships:

  • Member of Institute of Electrical and Electronics Engineers (IEEE)
  • Member of ACM SIGSPATIAL
Work Experience
  • 2021 鈥 Present: Assistant Professor, EECS Department, South 糖心视频 State University
  • 2017-2021: Graduate Research Assistant, Department of Computer Science, Virginia Tech

Research and Scholarly Work

Areas of Research
  • Spatial data mining
  • Spatiotemporal event analysis
  • Graph neural networks
  • Urban computing
  • GeoAI
Grants
  1. NSF CRII: Aug. 1, 2024 - July 31, 2026
    1. Role: Sole PI
    2. Total Award: $174,734
    3. Title: CRII: III: Learning Spatiotemporal Impacts of Text-enriched Traffic Events with Injection of Interpretability from Graph Neural Networks and Physics-Informed Machine Learning
  2. NSF: Sept. 1, 2024 - Aug. 31, 2026
    1. Role: PI
    2. Total Award: $300,000
    3. Title: EAGER: PBI: Collaboration Patterns and Socio-Economic Impacts Analysis in Emerging Science and Technology with Machine Learning Algorithms
  3. SDSU RSCA Program: July 1, 2024 - June 30, 2025
    1. Role: PI
    2. Total Award: $10,118
    3. Title: Towards Enhancement of Spatiotemporal Event Analysis: A Graph Transformer-based Location Representation Learning Solution
  4. SDSU SPARC Program: July 1, 2023 - June 30, 2024
    1. Role: Co-PI
    2. Total Award: $12,000
    3. Title: Analyzing Deaths of Despair Determinants in Rural Areas with Spatiotemporal Considerations
  5. NSF RII Track 2: Sept. 15, 2023 - Aug. 31, 2027
    1. Role: SP
    2. Total Award: $750,000
    3. Title: Collaborative Research: RII Track鈥2 FEC: STORM: Data鈥怐riven Approaches for Secure Electric Grids in Communities Disproportionately Impacted by Climate Change
Mailing Address:
Daktronics Eng Hall 123
Electrical Engineering/Computer Science-Box 2222
University Station
Brookings, SD 57007
Office Location:
Daktronics Engineering Hall
Room 123
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Related Links
Associated Areas
Lab Project
McComish Department of Electrical Engineering and Computer Science