The Masters in Industrial Engineering and Management is a research-intensive program curated to offer students a range of engineering subjects and quantitative analysis techniques. These include planning, design, control, and management of complex organizations and systems.
The program is designed with a view that an industrial engineer must consider not only the behavior of inanimate objects governed by the laws of physics, but also the behavior of people as they interface with inanimate objects and interact among themselves, and therefore, the curriculum includes the basics of engineering, economics, behavioral sciences, and management.
The master’s program equips engineering students with a know-how that stresses innovation, how to find optimal processes to enhance productivity in manufacturing and service industries. The students study a wide range of quantitative and qualitative topics that provides them a comprehensive education in subjects such as operations research and statistics, performance measurement and productivity, autonomous vehicles and robotics, engineering production and management, machine learning and artificial intelligence, inventory control, and distribution systems, ergonomics and human factors.
Taught entirely in English, the two-year research-intensive program offers students with three areas of specialization –
· Human Factor Engineering
· Knowledge and Data Engineering
Autonomous systems and robotics focus on defining capabilities, motion planning of autonomous vehicles, navigating robots in a plane under uncertainty, searching for targets in probabilistic space, the motion of robotic swarms and topological methods, and their uses for analyzing single motion and cluster motion.
Human factor engineering focuses on work environment design, effective movement, movement volumes, measurement, performance improvement, and efficiency characterizations, physiological, psychological, and functional stress, human performance ability, human-looking interfaces and limitations, and decision-making in control and monitoring systems.
Knowledge and data engineering, one of the recently emerging fields combines algorithms for dealing with vast information, statistical prediction methods, machine learning, and “deep learning” algorithms to enable the transformation of raw data to models that predict, foretell, and draw conclusions.