Data has evolved from purely scientific artifacts to a core part of public reasoning, decision-making, and communication. For example, during the COVID-19 pandemic, data were used to justify actions and drive understanding at levels ranging from the Centers for Disease Control to journalists at the New York Times to citizen scientists on Twitter. These efforts universally relied on visualizations to provide an accessible and effective way to explore and communicate data. However, how we represent data can dramatically influence the conclusions that we draw from that data. While visualization design depends on the visual channels used, visualization types, or visual tasks, we need a concrete human-centered system to understand the intersection of these factors to create a task-optimized visualization. In this talk, we address this gap by developing perceptual frameworks built on empirical models, data-driven metrics, and topology-based systems that consider design decisions and the task to maximize statistical task efficacy. This talk will describe systems, techniques, experiments, and user studies to model visualization design optimization and data transformation for low-level visual tasks. Further, the talk will elaborate on utilizing the framework to provide less ambiguous data presentations, leading to better quality and higher confidence in decision-making. Finally, the talk will introduce the ongoing and future work on driving effective data exploration using the proposed perceptual and conceptual models through cognitive and perception science.

Ghulam Jilani Quadri is a tenure-track Assistant Professor at the School of Computer Science at the University of Oklahoma. Ghulam was previously a Postdoctoral Research Associate and CRA/CCC/NSF Computing Innovation Fellow in the Department of Computer Science at the University of North Carolina-Chapel Hill, working with Dr. Danielle Albers Szafir. Quadri earned his Ph.D. in Computer Science & Engineering from the University of South Florida in 2021, advised by Dr. Paul Rosen. He holds an M.S. in Computer Science from the University of South Florida and a B.E. in Computer Engineering from the University of Mumbai. Quadri's research lies at the intersection of Information Visualization, HCI, ML Models, and perception & cognition. His primary goal is to create a perceptual and human-centered framework to optimize visualization design, improving decision-making quality and confidence, while providing objective guidance for designers. His research contributions have received significant support, funding, and recognition, including honorable mentions at the VAST Challenge 2017, an NSF Computing Innovation Fellowship in 2021, the IEEE VGTC Best Dissertation Award in 2022, and honorable mentions for the Best Paper Award at IEEE VIS 2023, Best Short Paper Award at CGF/VGTC EuroVis 2024.