Health Sciences 650 -- Curriculum / Collection at UMMA

Data Science and Predictive Analytics

School of Nursing; Medical School

Faculty member: Ivo Dinov (Health Behavior and Biological Sciences; Computational Medicine and Bioinformatics)

Students in this course will work on projects to train machine-intelligent systems to learn, model, analyze, and synthesize images and other complex data sets. These works of art were chosen because they pose specific challenges to computer vision, information extraction, and data analytics. With an image like Khaled al-Sa’ai’s calagraphic mural, artificial intelligence might have difficulty determining which of the lines, shapes, and shades define arabic letters a human would immediately recognize. The display provides students with an opportunity for hands-on exploration of obfuscated information and contrasts the differences between human and machine visual cognition.


We want to see things in the art that might not have been visible to us without the computer’s vision.

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Edge Detection, Denoising, and Other Ways to Look at Art

In Professor Ivo Dinov’s Data Science and Predictive Analytics class, students are learning how to manage, analyze, and make sense of Big Data in the real world. You’ve probably associated large, complex datasets with healthcare data, scientific information, or all of the information about viewing preferences of Netflix subscribers, but art?

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Works included in this collection

Exhibition Support

Lead support for this exhibition is provided by the University of Michigan Office of the Provost, Erica Gervais Pappendick and Ted Pappendick, the Eleanor Noyes Crumpacker Endowment Fund, and P.J. and Julie Solit.

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