Sean Donegan
Sean Donegan's interests focus on clarifying the decision making process for engineers by extracting actionable, quantifiable information from materials data, through application of modern techniques in machine learning and data analytics. He is currently involved in researching methods for understanding process-structure-property relationships in additive manufacturing, quantifying materials microstructure, and data fusion of multiple modes of materials characterization data.
His other research passion is exploring autonomous methods for materials characterization. Advances in robotics and automated microscopy allow for the collection of huge amounts of raw data; the missing components are smart, targeted systems that collect data only where they are needed, and are capable of adapting when new information is recognized. How such autonomous agents can be designed and integrated with the necessary control hardware is an area that he is actively exploring.
His other research passion is exploring autonomous methods for materials characterization. Advances in robotics and automated microscopy allow for the collection of huge amounts of raw data; the missing components are smart, targeted systems that collect data only where they are needed, and are capable of adapting when new information is recognized. How such autonomous agents can be designed and integrated with the necessary control hardware is an area that he is actively exploring.