Course: Mathematics in imaging: basics and applications

Sung Ha Kang works in applied mathematics, specifically in variational approaches to image processing, with various applications such as image denoising, debarring, imaging through turbulence, curvature based models, image segmentation and optimization.  Following a PhD in Applied Mathematics from UCLA, she held an Assistant Professorship at University of Kentucky.

In 2008 she joined Georgia Tech, where her research has been supported through NSF and the Simons Foundation. She holds leadership roles in the GT-Math Applications Portal, and the Computational Science and Engineering program at Georgia Tech.