Colloquium

UW-ISyE looks forward to welcoming Dr. Santanu Dey, Professor at H. Milton Stewart School of Industrial and Systems Engineering at Georgia Institute of Technology. We develop two general-purpose strong dual formulations for binary MINLPs, motivated by sensitivity analysis and distributed computation. For mixed binary quadratic programs (MBQPs), we show that the copositive dual of Burer’s completely positive...

UW-ISyE looks forward to welcoming Minshuo Chen, assistant professor with the Department of Industrial Engineering & Management Sciences at Northwestern University Diffusion models achieve state-of-the-art performance in various high-dimensional data modeling tasks. These empirical successes challenge conventional wisdom while raising critical concerns. On the one hand, in high-dimensional applications, diffusion models’ strong performance appears to circumvent the...

UW-ISyE looks forward to welcoming Roy Dong Assistant Professor in the Industrial & Enterprise Systems Engineering department at the University of Illinois at Urbana-Champaign. As data-driven methods are deployed in real-world...

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 often purchase complementary products across categories, which makes category-level decisions interdependent. Second, inventory is limited and customers arrive over time, so product availability changes dynamically...

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 and parameters can be unknown or ambiguous, creating significant challenges for algorithm design and stepsize selection. In this talk, I will present a novel class...

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 aims to provide an overview of recent advancements in GPU-based first-order methods for LP, with a particular focus on the design and development of cuPDLPx....

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...

As artificial intelligence becomes increasingly embedded in work systems, questions of trust extend beyond whether people accept or rely on algorithms to how humans and AI jointly perform, adapt, and sustain trustworthy decisions over time. In this talk, I present research that frames trustworthiness as a system-level property built through the iterative design, evaluation, and...