Speaker: Rayadurgam Srikant

Abstract: 

Crowdsourcing is a popular method used to estimate ground-truth labels by collecting noisy labels from workers. In this work, we are motivated by crowdsourcing applications where each worker can exhibit two levels of accuracy depending on a task’s type. Applying algorithms designed for the traditional Dawid-Skene model to such a scenario results in performance which is limited by the hard tasks. Therefore, we first extend the model to allow worker accuracy to vary depending on a task’s unknown type. Then we propose a spectral method to partition tasks by type. After separating tasks by type, any Dawid-Skene algorithm (i.e., any algorithm designed for the Dawid-Skene model) can be applied independently to each type to infer the truth values. We theoretically prove that when crowdsourced data contain tasks with varying levels of difficulty, our algorithm infers the true labels with higher accuracy than any Dawid-Skene algorithm. Experiments show that our method is effective in practical applications. Joint work with Seo-Taek Kong and Saptarshi Mandal.

Speaker: Kira Barton

Abstract: 

Manufacturing has undergone significant changes over the past five-ten years thanks to technological advancements that have been leveraged to meet a diverse set of customer requirements driven by global and societal needs. Conventional manufacturing control strategies were typically designed for robustness and speed within a controlled and well-regulated environment. However, recent demands for customization and agility coupled with big data investments have provided an opportunity for more learning-based methods to be introduced. Data driven strategies have long provided a means of harnessing information to enhance the performance of these complex systems. This talk is motivated by real-world interest from industry in understanding how to combine data-based learning and experiential knowledge to make intelligent decisions that can save time, money, and resources.

In this talk, we examine which aspects of manufacturing processes lend themselves to learning strategies and which bring additional challenges. We also explore cases in which learning has been applied in different ways to additive manufacturing processes in order to improve process knowledge and performance. Opportunities for additional integration of learning into the manufacturing domain will be discussed and open research questions for control-theoretic advancements will be highlighted.