This hands-on IntroSem course introduces students to the tools and methods engineers use to design, analyze, and validate sensing systems for sustainable communities. From bridges, buildings, and transportation networks to human and natural environments, modern infrastructure increasingly relies on sensors, data, and artificial intelligence (AI) to monitor system behavior in real time. In this course, students explore how sensing technologies, AI, and data-driven methods reveal hidden patterns in human, infrastructure, and environmental systems, and how these insights support more sustainable, resilient, and adaptive communities. Through interactive lectures and case studies, students develop foundational knowledge of sensing technologies, such as vibration and imaging sensors, and core principles of data processing and introductory AI. The course emphasizes critical thinking and validation. Students examine the capabilities and limitations of sensors, data, and AI through real-world applications that highlight their societal and environmental impacts. They learn not only how to collect and analyze data, but also how to question it, asking: What are the potentials and limitations of sensors and data? How reliable are AI outputs? What are the implications of data analysis results for decision-making? In hands-on labs and collaborative team projects, students design and prototype their own sensing systems, collect and interpret real-world data, and propose engineering solutions that improve the sustainability, efficiency, and resilience of the built environment.
3 units · Letter (ABCD/NP) · GER: WAY-AQR
This hands-on IntroSem course introduces students to the tools and methods engineers use to design, analyze, and validate sensing systems for sustainable communities. From bridges, buildings, and transportation networks to human and natural environments, modern infrastructure increasingly relies on sensors, data, and artificial intelligence (AI) to monitor system behavior in real time. In this course, students explore how sensing technologies, AI, and data-driven methods reveal hidden patterns in human, infrastructure, and environmental systems, and how these insights support more sustainable, resilient, and adaptive communities. Through interactive lectures and case studies, students develop foundational knowledge of sensing technologies, such as vibration and imaging sensors, and core principles of data processing and introductory AI. The course emphasizes critical thinking and validation. Students examine the capabilities and limitations of sensors, data, and AI through real-world applications that highlight their societal and environmental impacts. They learn not only how to collect and analyze data, but also how to question it, asking: What are the potentials and limitations of sensors and data? How reliable are AI outputs? What are the implications of data analysis results for decision-making? In hands-on labs and collaborative team projects, students design and prototype their own sensing systems, collect and interpret real-world data, and propose engineering solutions that improve the sustainability, efficiency, and resilience of the built environment.
Offered in Autumn 2026 at Stanford University.