Colloquium

Assortment and inventory decisions lie at the core of supply chain and retail operations. In practice, these decisions face two fundamental challenges arising from complex customer choice behavior. First, customers...

First-order methods are widely used to tackle data science and machine learning problems with complex structures, such as nonconvexity, nonsmoothness, and stochasticity. However, in many real-world scenarios, the problem structure...

The rapid progress in GPU computing has revolutionized many fields, yet its potential in mathematical programming, such as linear programming (LP), has only recently begun to be realized. This talk...

Technology is evolving rapidly, and human interaction with autonomous technologies is no longer confined to one-to-one decision-support settings. Intelligent agents are increasingly working alongside groups of people in diverse contexts such as defense, transportation, and manufacturing. Consequently, there is a growing need to design trust- and behavior-aware adaptive agents that allow humans and autonomous systems...

In contemporary machine learning, realistic models exhibit increasing nonconvexity and overwhelming overparameterization. This nonconvex nature often leads to numerous undesirable or "spurious" local solutions, while overparameterization exacerbates the risk of overfitting. Yet, simple “short-sighted” algorithms, such as gradient descent (GD) or its variants, often find the needle in the haystack: they converge to the correct,...

Most advances for the integration of 3D printing (3DP) into production settings have focused on small-scale 3DP with manufacturing of components such as aerospace fuel nozzles, aircraft’s hydraulic components, and military hardware to name a few. But when it comes to large-scale applications 3DP research has been minimally explored. Defining large-scale 3DP as additive manufacturing...