An overview of the research conducted by Dr. Sebastian Pokutta, associate professor at the Stewart School of Industrial & Systems Engineering and deputy director of the Center for Machine Learning @ GT, as part of the SCL Interdisciplinary Research Center seminar series.

DESCRIPTION OF THE SEMINAR
In this lecture we explore the possibilities of machine learning in supply chain and logistics. We will see that modern machine learning methods, often in a black box fashion, allow us to move from model-driven decision making to data-driven decision making. This results in a high degree of flexibility and agility, which is increasingly important in fast-paced supply chain and logistics environments. We will explain the basis of this paradigm and look at specific examples from the supply chain and logistics context.

ABOUT SEBASTIAN POKUTTA
The research of Dr. Pokutta focuses on combinatorial optimization and polyhedral combinatorics, focusing in particular on cutting plane methods and extended formulations. His industrial research interests are in the areas of optimization and machine learning in the context of analytics, with a focus on real-world applications, both in established industries and in emerging technologies. Application areas include, but are not limited to, supply chain management, finance, cyber-physical systems and predictive analytics. So far, Dr. Pokutta successfully deployed the analysis methodology in twenty practical projects.

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