David Stork

Scientistat Consultant
SF Bay Area,
United States
Focus area: AI (Machine Learning, Neural Networks, Deep Fakes), Archiving, Conservation, Art History, Art Theory, Critical Theory, Cognitive Science, Neuroscience, Neuroaesthetics, Computer Science, Engineering, Data Art, Science, Digital Culture, Digital Humanities, Fine Arts 2D, Generative Practices, Generative Art, Math, Math Theory, Optics, Visual Perception, Visual Culture, Visual Studies

I continue to work on the application of sophisticated computer vision and image analysis to address problems in the history and interpretation of fine art paintings and drawings—what I'm calling computer-assisted connoisseurship.  I teach a number of courses on the subject at Stanford University, including Computer vision and image analysis of art (CS), Image processing of art (EE), and Statistical analysis of art (Statistics), all out of my forthcoming book Pixel & paintings:  Foundations of computer-assisted connoisseurship (Wiley, 2022 exp).  I created the world's first scholarly conference in this field, which is now called Computer vision and image analysis of art (CVAA) at IS&T's Electronic Imaging Conference.  I and my colleagues have pioneered many techniques, and our current focus is on inferring simple meanings based on fine art images (primarily paintings), such as the meanings in vanitas paintings of the Dutch Golden Age, or the morals and lessons in religious art.