Artificial Intelligence for Smart Cities
Led by Jinwoo Jang, Ph.D.
Jinwoo Jang is an Assistant Professor in the Department of Civil, Environmental and Geomatics Engineering (CEGE) at Florida Atlantic University. He also serves as a faculty fellow at the Institute for Sensing and Embedded Network Systems Engineering (I-SENSE). He obtained his Ph.D. degrees in Civil Engineering and Engineering Mechanics from Columbia University. He has been actively engaged in smart city research, powered by Artificial Intelligence (AI), the Internet of Things (IoT) sensing, and data science. His ongoing federal-funded research activities include AI-embedded IoT sensing for smart streetscapes, human behavior/cognition sensing for older drivers, data mining of city-scale telematics data, and research traineeship in data science.
Throughout the project, REU participants will deeply engage with AI and IoT technologies to enhance city asset management, public safety, and environmental sustainability at a streetscape level. Ultimately, this project aims to create streetscape applications that harness real-time, hyper-local intelligence to advance our cities' livability, resiliency, and safety. This REU project perfectly aligns with the vision of the NSF Engineering Research Center (ERC) for Smart Streetscapes (CS3), which is the NSF's major research investment in shaping upcoming significant research areas for the next decade.
While exploring novel engineering solutions for smart cities, REU participants will have tangible research experience in smart cities, ranging from innovative sensor data collection to cutting-edge data science techniques. REU participants will be involved in developing IoT sensing platforms, data analytics, visualization, and machine learning to establish holistic approaches for real-world smart city challenges and problems. The REU participant will learn how to get data, how to process data, and, more importantly, how to integrate data to draw greater impacts of data on cities. In addition to advanced sensing and embedded network applications, the participant will deepen fundamental knowledge in data science through this project, such as machine learning, optimization, information theory, and signal processing. The project will provide a meaningful and enjoyable experience for the participant, supporting the participant's academic and research career.