Graduate Student Seminar
September 27, 2024
12:45 p.m. ET
Wean Hall 7500
Unveiling the Future of Color Matching: PPG Digitization, Color Physics, and AI insights
At PPG Industries, our mission is to Protect and Beautify the World, and this presentation focuses on color and its significance in the paint and coatings industry. While much focus is placed on protection, beauty is not merely a visual perception, but rather a material property that is integral to product development, design, and consumer preferences. As a data scientist overseeing color AI across various Strategic Business Units (SBUs) at PPG, this talk will highlight the advances made in color matching through digitization, color physics, and the integration of artificial intelligence. By incorporating the chemistry and physics of color, we can develop models and algorithms that enhance the accuracy of color matching, which is critical to a diversity of applications. This discussion will explore the fundamental concepts of color physics, encompassing phenomena such as light absorption, scattering, and transmission, and their contributions to color perception. Additionally, models pertaining to the subjective perception of color will be examined, with the aim of incorporating them into inference algorithms. The integration of artificial intelligence (AI) in color matching will be discussed, and insights will be shared regarding how AI algorithms are being harnessed at PPG to analyze color data. The challenges and opportunities associated with implementing AI in color matching will be discussed, highlighting its potential to drive innovation at PPG.
Chris Childs, PPG Industries
Childs is currently a data scientist at PPG Industries, where his work primarily focuses on the incorporation of machine learning techniques to model and optimize complex material systems. He graduated with a PhD in Chemistry from Carnegie Mellon University, specializing in the optimization of cementitious compounds with AI. During his doctoral studies, he worked with Prof. Newell Washburn. Prior to joining PPG, Dr. Childs worked as a research engineer at Ansatz AI, which was co-founded by Prof. Washburn and Prof. Barnabas Poczos of the CMU Machine Learning Department. There he focused on developing formulation AI methodologies for understanding and optimizing complex material systems. This involved leveraging chemical and physical material properties obtained from both experimental and computational modeling. At PPG, his work continues to utilize similar approaches. First employing algorithmic approaches, he utilized this information for single and multi-objective inference, allowing for the optimization of material and processing conditions. While his work encompasses various aspects of material systems, his primary focus at PPG is on color optimization.
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