Industrial engineering, operations research, and systems engineering are fields of study intended for individuals who are interested in analyzing and formulating abstract models of complex systems with the intention of improving system performance. Unlike traditional disciplines in engineering and the mathematical sciences, the fields address the role of the human decision-maker as key contributor to the inherent complexity of systems and primary benefactor of the analyses.
Georgia Tech pursues leading-edge research with industry, government, and community partners.
At ISyE, we are a national leader in 10 core fields of specialization: Advanced Manufacturing, Analytics and Machine Learning, Applied Probability and Simulation, Data Science and Statistics, Economic Decision Analysis, Energy and Sustainable Systems, Health and Humanitarian Systems, Optimization, Supply Chain Engineering, and Systems Informatics and Control.
ISyE's faculty and staff members strive to provide a world-class educational experience for the Stewart School's undergraduate and graduate students, and to forge long-lasting relationships with ISyE alumni and industry partners. If you have benefited from a connection with an ISyE faculty or staff member, feel free to take a moment to send a thank-you note to that person via this web form.
You can stay in touch with all things ISyE through our news feed, by reading one of our publications, or attending one of our upcoming events. ISyE employs some of the world’s most experienced researchers in their fields who enjoy sharing their perspectives on a wide variety of topics. Our faculty is world-renowned and our students are intellectually curious. Our alumni can be found around the globe in leadership positions within a wide variety of fields.
Qian Wang is currently a Ph.D. student in Machine Learning at H. Milton Stewart School of Industrial and Systems Engineering (ISyE) at Georgia Tech. His research focuses on the analysis of high-dimensional functional data including profiles, images and point cloud data using statistical machine learning tools for anomaly detection, quality control or root cause analysis purposes in manufacturing.