Cheng-Kang Ted Chao 趙城綱

I am a fourth-year Computer Science PhD student in CraGL at George Mason University, where I have wonderful time working with Yotam Gingold. Throughout my PhD journey, I worked as a research intern under Jose Echevarria during my internship at Adobe Research. I also was an applied research intern at Tencent Pixel Lab, working with Ran Zhang and Changxi Zheng.

Currently, I am a research intern at Adobe Research working with Li-Yi Wei and Rubaiat Habib Kazi.

Before moving to Virginia, I was a Master's student in Computational Science and Engineering at Georgia Institute of Technology, advised by Edmond Chow. I received my Bachelor's degree in Mathematics from National Central University, Taiwan.

Email  /  CV  /  Github

I focus on developing efficient algorithms for image and shape manipulation to address various design challenges, such as stylization and editing. I am particularly interested in leveraging a combination of optimization, machine learning, computer vision, partial differential equations, and numerical linear algebra to achieve this goal.

We developed a constraint-driven sparse optimization for color editing, uniting palette-based framework with lightness manipulation.

We extended palette-based editing framework to utilizing semantic soft segments within our proposed palette hierarchy. We also developed an approach to compute sparser weights without sacrificing much spatial coherence.

We wrote a short survey paper summarizing some state-of-the-art (mainly focused within recent two years) editing and generation algorithms for images and shapes.

Automatic artistic posterization with user's control for different levels of abstraction, color diversity, and image recoloring.

 May.28.2024:   I started as a research intern at Adobe Research in San Jose.
 Aug.09.2023:   I presented our work ColorfulCurves at SIGGRAPH 2023 in Los Angeles.
June.30.2023:   I gave a talk at EGSR 2023 in TU Delft on our work LoCoPalettes.
June.20.2023:   I started as an applied research intern at the Tencent Pixel Lab in New York.
May. 25.2023:   I attended Capital Graphics 2023.

I found the below are pretty useful for research use, especially when you need to quickly work on mathematical derivations but forgot some of the rules...

1.   The Matrix Cookbook: a very powerful book having different kinds of derivatives lookup
2.   Matrix Calculus: an interactive tool for computing derivatives for optimization problems

I enjoy taking black-and-white photography when traveling. Check out my instagram for more photos.

The code of this website is adapted from Jon Barron. Do not scrape the HTML from this page itself,
as it includes analytics tags that you do not want on your own website — use the github code instead.