Seongjin Choi (최성진)
Scholar

Seongjin Choi (최성진)

Google Scholar ID: tyLWFk4AAAAJ
University of Minnesota, Twin Cities
AI in TransportationUrban MobilityGenerative AISpatiotemporal Data MiningTraffic Simulation
Citations & Impact
All-time
Citations
604
 
H-index
11
 
i10-index
14
 
Publications
20
 
Co-authors
0
 
Publications
20 items
Browse publications on Google Scholar (top-right) ↗
Resume (English only)
Academic Achievements
  • Recent publications include: 'A Gentle Introduction and Tutorial on Deep Generative Models in Transportation Research' in Transportation Research Part C; 'Probabilistic Traffic Forecasting with Dynamic Regression' in Transportation Science; 'Scalable Dynamic Mixture Model with Full Covariance for Probabilistic Traffic Forecasting' in Transportation Science.
Research Experience
  • Conducted research at AIxMobility Lab and Smart Transportation Lab.
Education
  • Received Ph.D. from the Department of Civil and Environmental Engineering at Korea Advanced Institute of Science and Technology, supervised by Professor Hwasoo Yeo; completed Bachelor's and Master's degrees from the same department and institute; was a Postdoctoral Researcher in the Department of Civil Engineering at McGill University in Canada, with Professor Lijun Sun.
Background
  • I'm an Assistant Professor in the Department of Civil, Environmental, and Geo-Engineering at the University of Minnesota, Twin Cities. My research interests are broad and interdisciplinary, encompassing Urban Mobility Data Analytics, Spatiotemporal Data Modeling, Deep Learning & Artificial Intelligence, and Connected Automated Vehicles (CAV) & Cooperative-ITS. I am particularly driven by the desire to optimize urban mobility and contribute to the development of a sustainable and efficient urban transportation system. My work involves utilizing data analytics to draw valuable insights from urban mobility data and applying cutting-edge AI technologies in the field of transportation.
Miscellany
  • Looking for 1-2 PhD students (and/or a Postdoc) for 2025 Spring/Fall who are excited about machine learning for urban transportation and mobility data.
Co-authors
0 total
Co-authors: 0 (list not available)