Ponente: Dr. Lace Padilla (Assistant professor in the Cognitive and Information Sciences department at the University of California Merced)
Uncertainty is inherent to all data collection and present in any modeling process. Unfortunately, uncertainty is highly challenging for both the general public and trained experts to understand, which is why effectively conveying uncertainty in scientific findings is critical. In this talk, we will discuss modern empirically tested ways of conveying uncertainty in scientific findings. We will also discuss cognitive theories that describe reasoning with uncertainty. This talk will focus on best practices in information visualization to help viewers convey their findings more effectively and become more informed information consumers.
Dr. Lace Padilla is an assistant professor in the Cognitive and Information Sciences department at the University of California Merced. She received a Ph.D. in Cognitive and Neural Sciences and an MFA in Design from the University of Utah. Padilla and collaborators were recently awarded an NSF RAPID award to study uncertainty in COVID-19 data visualizations. She also received an APA early career researcher award, an NSF Postdoctoral fellowship, and a Visionary Grant funded by NASA.