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Thanks to the Georgia Tech-Savannah campus, coastal area companies and supply chain and logistics professionals have local access to Georgia Tech's renowned professional education curriculum;…

Model-Driven Engineering (MDE) is meant, above other usages mainly in software design, to support the process of system design, especially the transitions and the coherency between models of…

Modern warehouses, factories and supply chains are becoming enormously complex systems. Historically, the design and control of these systems has depended upon evolution and refinement over time to…

This event will combine the traditional lecture and a live-recorded interview with the producers of the “Lost in the Stacks” podcast, as Souther reflects on his experience in modeling…

To derive a theory for gradient descent methods, it is important to have some understanding of the energy landscape. In this lecture, an overview of existing results is given. The second part of the…

We outline the theory underlying the recent bounds on the estimation risk of deep ReLU networks. In the lecture, we discuss specific properties of the ReLU activation function that relate to skipping…

E-commerce, globalization and urbanization, combined with new technologies and business models, are disrupting industry stalwarts. Today, no company is immune from disruption. How should…

Semiconductor and plasmonic nanocrystals are known for their size and shape dependent photo-luminescence and localized surface plasmon resonances, respectively. In this talk, I will describe the use…

Why are deep networks better than shallow networks? We provide a survey of the existing ideas in the literature. In particular, we discuss localization of deep networks, functions that can be easily…

We start with the universal approximation theorem and discuss several proof strategies that provide some insights into functions that can be easily approximated by shallow networks. Based on this, a…

Lecture 1) Survey on neural network structures and deep learning There are many different types of neural networks that differ in complexity and the data types that can be processed. This lecture…

We will discuss a general global perspective, and then a North America focus on current and future supply chain challenges relative to infrastructure constraints. An overview of omni-channel dynamics…

In this talk we construct a net around the unit sphere with good properties. We show that with exponentially high probability, the value of |Ax| on the sphere can be approximated well using this net,…

Stochastic Gradient Descent-type (SGD) algorithms have been widely applied to many non-convex optimization problems in machine learning, e.g., training deep neural networks, variational Bayesian…

Selfish behavior can often lead to suboptimal outcome for all participants, a phenomenon illustrated by many classical examples in game theory. Over the last decade we have studied Nash equilibria…

Urban logistics automation is collapsing traditional boundaries between storage and delivery, extending the warehouse presence in the built environment. At the same time, the growth of e-commerce…