Abstract:
To democratize visualization, especially to empower data-intensive domain scientists via intuitive uses of novel technical advances, I study means to advance use-inspired basic research to make technologies so usable that automating a task in a human-machine teaming fashion is made easier than performing or teaching the same task to a human and that humans learn from machines. In this talk, I will briefly present recent results integrating to continue to advance perceptually accurate visualizations to contribute to real-world uses in brain science and quantum physics. I will then discuss our new methods to quantify AI leveraging systematic measurements. Finally, I will present the on-going effort on reverse engineering visualizations for AI training data preparation and acquisitions to benefit the public to learn from historical documents.
The Presentor:
Jian Chen, Ph.D., is an Associate Professor in the Department of Computer Science and Engineering at The Ohio State University. Her research centers on visualization, immersive interaction, and human-AI teaming. Outside of computer science venues, Chen publishes work on Brain Science. Before joining The Ohio State University, Chen was a tenure-track Assistant Professor at the University of Maryland, Baltimore County, and earned University Presidential Teaching Innovation Award. Outside of research and teaching, she enjoys serving as an area paper chair for IEEE VIS 2021 and 2022, a voting member of IEEE Visualization Academy, and the Vice Chair for Conferences on the IEEE Visualization and Graphics Technical Community (VGTC), with a mission to improve conference experiences for the graphics and visualization communities.