Events

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...