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.
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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.
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Hassan obtained his MS in Management Science and Engineering from Columbia University. During his time at Columbia, he worked as a part-time analytics consultant for several companies. In his Master’s Thesis he worked on the Assortment Optimization problem from a risk perspective, where the objective was minimizing the risk that rises from the randomness of peoples' choices. He developed exact algorithms and optimization models to determine an optimal risk-averse assortment that does not encounter large revenue deviations.
Hassan is currently an Operations Research PhD student at the H. Milton Stewart School of Industrial and Systems Engineering (ISyE) at Georgia Tech. His research lies at the intersection of combinatorial and convex optimization. In particular, he is exploiting combinatorial structure inherent in some optimization problems to eliminate bottlenecks and speed up convex optimization algorithms.