Lecturers

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.

Course: Using PDE's in image analysis

Anthony Yezzi is a professor in the School of Electrical and Computer Engineering of Georgia Tech. Professor Yezzi was born in Gainsville, Florida and grew up in Minneapolis, Minnesota. He obtained both his Bachelor's degree and his Ph.D. in the Department of Electrical Engineering at the University of Minnesota with minors in mathematics and music. After completing his Ph.D., he continued his research as a post-Doctoral Research Associate at the Laboratory for Information and Decision Systems at Massachusetts Institute of Technology in Boston, MA. His research interests fall broadly within the fields of image processing and computer vision. In particular he is interested in curve and surface evolution theory and partial differential equation techniques as they apply to topics within these fields (such as segmentation, image smoothing and enhancement, optical flow, stereo disparity, shape from shading, object recognition, and visual tracking)

Course: Parallel and Distributed Algorithms for Large-Scale Optimization

Wotao Yin is a professor in the Department of Mathematics of UCLA. He works on computational optimization and its applications in image processing, machine learning, and other data science problems. He received his B.S. in Mathematics from Nanjing University in 2001, and then M.S. and Ph.D. in Operations Research from Columbia University in 2003 and 2006, respectively. During 2006 - 2013, he was with Rice University. He won NSF CAREER award in 2008, Alfred P. Sloan Research Fellowship in 2009, Morningside Gold Medal in 2016. He invented several well-known optimization algorithms, operator splitting methods, and parallel schemes. He has been leading the research of optimization algorithms for large-scale problems. His methods and algorithms have found very broad applications across different fields of science and engineering.