Learning Pigment Signatures from Multimodal Data

Timeline

2026

Description

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.  

View the team's poster here:

View more here: https://shirafaigen.wixsite.com/ml-mi-art-hub/projects

Team

Members

Frank Xie, Johan Nino Espino, Kyra Pahwa, Ronit Dey
 

Leaders

Project Leads: Martin Fischer and Shira Faigenbaum Golovin

Project manager: Keyu Li