Joshua Agar, Assistant Professor, Materials Science & Engineering
In materials science and physics more broadly there is a growing trend to conduct multimodal experiments (experiments that collect data from a variety of sources). The boon in data collection has left a majority of data collected under-analyzed leaving important physics left undiscovered. This project will develop machine and deep learning methods to discover actionable information from such data. This project will also consider how such models can be implemented on specialty AI hardware for real time analysis. The work will focus on materials problems as they provide unique ways to stress-test practical theories of machine and deep learning. Outcomes of this work have direct impacts on creating interpretable AI controlling fairness and bias, and creating autonomous control systems. The impacts of these theories can be adapted to solve problems in medicine and healthcare, resource management and logistics, and manufacturing and processing.