Introduction to automated vehicle systems with emphasis on transportation safety. Topics include historical background, advanced technologies in sensors and control, human factors design and application, research methodologies and state of the science, and policy and regulation.

The course introduces the theoretical background to hydrodynamics, gravity waves in the oceans, coastal processes and tides.

This course offers an examination of the principles of systems engineering (SE) and their application within engineering management contexts. Students will be introduced to the vocabulary and the core concepts and techniques (tools) of SE. Of particular focus is exploring how systems thinking, as a perspective, offers a valuable capability for holistically understanding and dealing with engineering management problems and challenges.

This course provides an overview of the fundamental concepts of basic accounting and finance, focusing on their application to managing engineering projects and organizations. Key topics covered include basic accounting terminology and methods, financial statements and how to interpret them, sources of finance available to businesses, engineering project accounting and financing, and personal and corporate financing basics.

This course is designed to familiarize graduate students with necessary skills to conduct engineering research, including its design, methods, and analyses. It will introduce and cover the following topics: identifying and selecting research questions, objectives, and hypotheses; conducting a literature review; selecting the appropriate research methods; analyzing the results using statistics in the R statistical environment; and writing a research plan or report. Students will also learn about the ethics of conducting engineering research.

Dynamic or sequential decision-making tackles a type of problems where decisions need to change over time to adapt to changing environments, occurring as a consequence of natural environmental dynamics and/or influence of prior decisions. These type of problems are encountered in a variety of fields.

Prescriptive analytics is the process of utilizing and analyzing data to make "optimal" decisions. In this course we will build optimization tools for data-driven decision making using Python along with the Gurobi optimizer (available freely for teaching purposes). This course will start with an introduction to both Python and optimization modelling. We will rapidly progress to building large linear programming and mixed integer programming models that are often used for decision making in data-intensive businesses.

Data analytics, statistical/machine learning, and predictive modeling are now used widely in all fields. The purpose of this course is to provide introductory knowledge that will help students understand the fundamentals of the field and use software packages to solve problems. The emphasis will be on applying the statistical methods to real data sets. The 600-level class will require students to explore topics in more depth.

Optimization in design. Methods of optimization; numerical and variational, linear and nonlinear. Advantages, disadvantages, restrictions, and use of various methods.

This course will cover both fundamental and cutting-edge topics in robotics. We will study the kinematics, dynamics, and autonomous control of various robotic systems (e.g., manipulators, mobile robots, and exoskeletons). The fundamentals will be studied in lectures and robot simulation exercises. State-of-the-art robotics research will be studied through paper reviews and discussion. A final research project will allow you to dive deeper into your choice of a topic covered in this course.