Data visualization is a powerful strategy for using graphics to represent data for effective communication and analysis. Unfortunately, creating effective data visualizations is a challenge for designers. The task is often open-ended, and thus usually involves an iterative process of trial and error, which by its nature, is time-consuming. Designers frequently seek feedback, but obtaining feedback from peers can be challenging, and alternatives like user studies or crowdsourcing is costly and time-consuming. This suggests the potential need for a tool that can swiftly provide design feedback. To that end, I create a virtual, human vision-inspired system that looks into the visualization design and provides feedback on it using various AI techniques. The goal is not to replicate an exact version of a human eye. Instead, it aims towards a practical and effective system that delivers design feedback to visualization designers. In doing so, I utilize advanced AI techniques, such as deep neural networks (DNNs) and large language models (LLMs). I introduce three systems that aim to focus on simulation, representation, and automation. To each work, I report on challenges and lessons learned about the key components and design considerations that help visualization designers.
5/ Bio: Sungbok Shin is a postdoctoral fellow at Team Aviz, Inria Saclay Center, Saclay, France. His research interests include Human-centered AI, Information Visualization, and Human-Computer Interaction, and AI applications. He received the Ph.D. degree in 2024 in Computer Science from the University of Maryland, College Park, MD, United States. He received his B.S. in Computer Science and Mathematics (dual degree), and M.Eng. in Computer Science from Korea University, Seoul, South Korea, in 2017 and 2019, respectively.

Sungbok Shin is a postdoctoral fellow at Team Aviz, Inria Saclay Center, Paris, France. His research interests include Human-centered AI, Information Visualization, Human-Computer Interaction, and AI applications. He received the Ph.D. degree in 2024 in Computer Science from the University of Maryland, College Park, MD, United States. He received his B.S. degree in Computer Science and Mathematics (dual degree), and M.Eng. degree in Computer Science from Korea University, Seoul, South Korea, in 2017 and 2019, respectively.