Social Event Detection and Forecasting withHeterogeneous Spatiotemporal Data

A C2SR Colloquia Series | Distinguished Webinar Series.
The Distinguished Speaker Webinar Series is aimed at advancing the state-of-the-art concepts and methods in artificial intelligence and cyber security areas. The series is jointly hosted by the Centers for Cyber Security and AI Research and the School of Electrical Engineering and Computer Science (SEECS) at the University of North Dakota College of Engineering & Mines.
Speaker Biography:
Dr Kaiqun Fu holds an assistant professorship in the Department of Electrical Engineering and Computer Science at South Dakota State University He earned his Ph D from Virginia Tech's Discovery Analytics Center in 2021 Dr Fu's academic pursuits encompass Spatial Data Mining, Machine Learning, Deep Learning, GeoAI Social Media Analysis, and Urban Computing His research projects have been supported by the National Science Foundation and South Dakota State His research projects have included social media mining for urban event detection, deep learning for urban perception, traffic impact analysis in smart cities, and forecasting emerging technology trends He has served as PC members for IJCAI/ACM SIGSPATIAL/SIAM DM/IEEE BigData.
About the Webinar:
The exploration and extraction of knowledge from urban environments are gaining significant attention in research The availability of diverse urban data, ranging from road traffic statistics and crime reports to social media content and street imagery, offers a wealth of information for comprehensive urban studies Recent years have witnessed remarkable progress in this essential field, marked by a surge in research publications and interdisciplinary collaborations This presentation will delve into three key areas of urban event detection and analysis 1 Detection and analysis of events through social media, focusing on transportation, cyberbullying, and civil unrest 2 Forecasting the impact of traffic incidents using sensor network data, featuring a model that predicts incident duration and identifies key temporal features through multi task learning 3 Examining urban safety and crime perceptions using street view images, highlighting the use of deep neural networks to establish a link between urban perception and crime

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