Global Arc

Cities generate unprecedented data from sensors, satellites, mobile devices and infrastructure. CEE 420 explores the transformative role of AI and machine learning in harnessing this data to model and improve urban systems. Students gain foundational skills in spatial data science, predictive modeling, graph learning, computer vision, and LLMs. Through hands-on coding and a semester-long project with the Municipality of Princeton, students tackle real-world challenges in autonomous mobility, climate resilience, and smart cities. We also critically examine algorithmic governance to navigate privacy, fairness, and bias.

This course examines the future of cities. The focus is on the intersection of rapid urbanization with the global grand challenges: climate, water, and energy. We start with an introduction to urbanization, how to characterize the shape and scale of cities, and the role they play at the global scale geophysically and socio-economically. We then examine heat, air and water flow in urban areas, focusing on how they induce urban heat islands, exacerbate floods, modify power consumption, and reduce thermal comfort. We conclude the course with an examination of how buildings and cities can be designed to be more sustainable, resilient and livable.

The senior thesis (498-499) is a year-long project in which students complete a substantial piece of research and scholarship under the supervision and advisement of a Princeton faculty member. While a year-long thesis is due in the student's final semester of study, the work requires sustained investment and attention throughout the academic year.

Students will learn different methods required for data analysis and interpretation of processes related to the natural and built environment. The emphasis will be on formulating questions, choosing appropriate statistical tools for a given problem, and drawing appropriate conclusion. The course will cover concepts related to statistical inference and common probabilistic models, linear regression, and expose the students to non-parametric statistics; the students will also learn how to perform these analyses using the R programming language. Statistical methods will be introduced through the use of hands-on analyses with real data.

This course introduces students to the principles, analysis methods, and applications of lightweight structures. The course bridges classical structural mechanics with modern advances in composites and architected materials. Topics include trusses, pressure vessels, fiber-reinforced composites, discrete lattices, and metamaterials. Through a combination of lectures, labs, and hands-on demonstrations, students will develop skills in static and dynamic analysis, modeling, and testing of various structures. These skills provide a strong foundation for innovative applications across engineering fields.

The course introduces fundamentals of optics, lasers, and Fourier transforms through lectures and hands-on activities. The topics include ray and wave optics, imaging and image processing, optical Fourier transforms, principles of lasers, and applications in nuclear fusion for renewable energy, environmental sensing, space exploration, ultrafast metrology, chemistry, and physics.

The senior thesis (MAE 498-499) is a year-long project in which students complete a substantial piece of research and scholarship under the supervision and advisement of a Princeton faculty member. While a year-long thesis is due in the student's final semester of study, the work requires sustained investment and attention throughout the academic year.

The senior thesis (MAE 498G-499G) is a year-long project in which students complete a substantial piece of research and scholarship under the supervision and advisement of a Princeton faculty member. While a year-long thesis is due in the student's final semester of study, the work requires sustained investment and attention throughout the academic year.

The senior thesis (498G-499G) is a year-long project in which students complete a substantial piece of research and scholarship under the supervision and advisement of a Princeton faculty member. While a year-long thesis is due in the student's final semester of study, the work requires sustained investment and attention throughout the academic year.

This course presents key ideas at the core of computational engineering, and provides the students with a foundation for further development of the discipline, as well as for the informed and expert use of advanced computational mechanics software. Basic topics in interpolation and approximation (polynomial and trigonometric basis); representation of curves and surfaces, accuracy and stability of numerical methods for ODEs and PDEs, and selected topics in numerical linear algebra and root finding of nonlinear systems.