Rate My Professor Ickjai Lee

Professor Lee's research interests include geospatial data mining encompassing clustering, association rules mining, and sequential mining; trajectory mining for travel patterns and animal movements; geocomputation, geo-engineering, geomatics, and spatial data handling; mobile augmented reality for learning and teaching; Voronoi tessellation and Delaunay triangulation applications; applied artificial intelligence and machine learning algorithms; intelligence and health informatics for the Tropics; market analysis through online map segmentation; geo-visualisation; Web 2.0; and map segmentation. He has produced 144 publications, with key recent contributions such as 'Advancements in preprocessing, detection and classification techniques for ecoacoustic data: A comprehensive review for large-scale passive acoustic monitoring' (2024, Expert Systems with Applications, with T. Napier et al.), 'Spatio-Temporal Contact Mining for Multiple Trajectories-of-Interest' (2024, IEEE Access, with A.R.S. Madanayake and K. Lee), 'Using mobile-based augmented reality and object detection for real-time Abalone growth monitoring' (2023, Computers and Electronics in Agriculture, with T. Napier), and 'A hybrid data gathering and agent based cognitive architecture for realistic crowd simulations' (2023, Journal of Simulation, with J. Sinclair and H. Suwanwiwat). His honors include lifetime membership in the ACM (2004) and ACM-SIGSPATIAL membership (2004). He currently supervises 10 PhD students on topics including deep learning, trajectory analysis, virtual reality, and medical imaging.