Rate My Professor Ickjai Lee

Professor Lee's research specializes in geoinformatics and intelligence informatics, with key interests in geospatial data mining, multiple classifiers, conceptual clustering, spatial clustering, location-based services, fuzzy modelling and reasoning, spatio-temporal data mining, computational intelligence, machine learning, big data analytics, visual analytics, geo-visualisation, Internet of Things, Voronoi tessellations, and conceptual spaces. His current work focuses on intelligence and health informatics for the Tropics. He has co-authored over 144 publications, including highly cited works such as 'CoroDet: A deep learning based classification for COVID-19 detection using chest X-ray images' (2021, 567 citations), 'Amoeba: Hierarchical clustering based on spatial proximity using Delaunay diagram' (2000, 160 citations), 'Autoclust: Automatic clustering via boundary extraction for mining massive point-data sets' (2000, 154 citations), 'Itinerary recommender system with semantic trajectory pattern mining from geo-tagged photos' (2018, 128 citations), and recent publications like 'Advancements in preprocessing, detection and classification techniques for ecoacoustic data: A comprehensive review for large-scale passive acoustic monitoring' (2024) in Expert Systems with Applications and 'Spatio-Temporal Contact Mining for Multiple Trajectories-of-Interest' (2024) in IEEE Access. His scholarship has earned over 2,100 citations. Awards include Best Paper at the 46th Hawaii International Conference on System Sciences (2012), Faculty Citation for Outstanding Contributions to Student Learning (2009 and 2006), Best Paper at the Pacific Asia Workshop on Intelligence and Security Informatics (2007), ACM lifetime membership (2004), and ACM-SIGSPATIAL (2004).