The Future is not Set: Visual Decision-Making with Predictive Models
Fumeng Yang
IRB 4105 or https://umd.zoom.us/j/95853135696?pwd=VVEwMVpxeElXeEw0ckVlSWNOMVhXdz09
Thursday, March 14, 2024, 11:00 am-12:00 pm 
Abstract
Predictions, such as COVID forecasts and AI recommendations, are now commonly seen in our daily lives and integrated into workflows. However, these predictions come with inherent imperfections—such as error and uncertainty—that raise significant challenges for individuals trying to understand them and make appropriate decisions. My research tackles the problems of trust in predictive models and decision-making. In this talk, I will focus on two areas: providing visual explanations to identify machine learning errors, and using uncertainty visualizations to build trust in election forecasts, both of which help lead to more appropriate decisions. I will also briefly discuss the application of perceptual science and virtual reality in presenting model predictions and aiding decision-making. The ultimate goal of my research is to ensure appropriate decisions for different user groups.
Bio
Fumeng Yang is a postdoctoral fellow at Northwestern University funded by the CRA/CCC's CIFellows program. She received her Ph.D. in Computer Science from Brown University and her M.Sc. in Computer Science from Tufts University. Her research is at the intersection of human-computer interaction and visual computing, focusing on visual explanation and uncertainty visualization for decision-making. She is also interested in perception and cognition as well as virtual and augmented reality. Her research has been recognized with one best paper award at IEEE VIS and three honorable mention awards at ACM CHI, IEEE VIS, and ACM IUI.
This talk is organized by Samuel Malede Zewdu