Historic artworks offer a unique window into the past, providing rich insight into history, culture, and tradition. Despite their profound significance, many works remain shrouded in mystery, raising questions about their origins, creators, and the circumstances of their production. Some of these questions can be addressed through advanced image acquisition techniques combined with machine learning. A team of students, led by an interdisciplinary group of faculty spanning the departments of chemistry, mathematics, biological engineering, and art history, will develop AI-based tools to analyze pigment signatures acquired through multimodal imaging systems. The students will identify key steps in the study of these signatures and subsequently develop data-driven, AI-based methods for their analysis.
Project Leads: Martin Fischer and Shira Faigenbaum Golovin
Project manager: TBD
