Executive Director of Academic Administration and Student Experience
Dima Nazzal is a Principal Academic Professional in the H. Milton Stewart School of Industrial and Systems Engineering at Georgia Tech. She is responsible for project-based learning in the Industrial Engineering undergraduate curriculum, including the capstone senior design course, and the cornerstone junior design course. She is also research director of the Center for Health and Humanitarian Systems. Prior to joining Georgia Tech, she was Director of Research and Development at Fortna, Inc., an Engineering Design and Consulting company.
Research: Her research focuses on modeling, design, and control of discrete event logistics systems, including healthcare delivery systems, manufacturing systems, and distribution systems. Her recent work has focused on election voting systems, higher education response to COVID-19, understanding and driving higher childhood vaccination rates in developing countries, modeling of collaborative robots in distribution systems; scheduling and dispatching policies in semiconductor manufacturing, and energy systems development. She has worked with companies, non-governmental organizations, and healthcare providers, including ExxonMobil, Emory University, Samsung, Emory University, Gates Foundation, and Walt Disney World. See here for relevant publications.
Teaching: Dr. Nazzal enjoys teaching courses in manufacturing, warehousing, and facility logistics system design and operations, as well as advising senior design teams. She is the recipient of multiple teaching awards including the Georgia Tech Women in Engineering Outstanding Teacher Award in 2015, and the Most Outstanding Faculty Member Award from the University of Central Florida IIE Student Chapter in 2011.
She received her Ph.D. in Industrial Engineering from Georgia Tech in 2006, her M.S. in Industrial Engineering from the University of Central Florida, and her B.S. in Industrial Engineering from the University of Jordan.
Georgia Institute of Technology
Modeling and analysis of discrete manufacturing flow systems using stochastic OR methods