Seeing What We (Should) Think Through Visualization Interaction
, Northwestern University
Date: Tuesday, October 16, 2018
Time: 1:00 PM to 2:00 PM
Location: Kiva Room (32-G449)
Event Type: Seminar
Room Description: Seminar Room
Host: Arvind Satyanarayan
Contact: Amy Shea-Slattery, 617-253-6002, email@example.com
Relevant URL: hci.mit.edu/hci-seminar.html
firstname.lastname@example.org, HCI-Seminar@lists.csail.mit.edu, email@example.com
TALK: HCI Seminar Series: Jessica Hullman: Seeing What We (Should) Think Through Visualization Interaction
Abstract: Charts, graphs, and other information visualizations amplify cognition by enabling users to visually perceive trends and differences in quantitative data. While guidelines dictate how to choose visual encodings and metaphors to support accurate perception, it is less obvious how to design visualizations that encourage rational decisions from a statistical perspective. I'll motivate two challenges that must be overcome to support effective reasoning with visualizations. First, people's intuitions about uncertainty often conflict with statistical definitions. I'll describe research in my lab that shows how visualization techniques for conveying uncertainty through discrete samples can improve non-experts' ability to understand and make decisions from distributional information. Second, people often bring prior beliefs and expectations about data-driven phenomena to their interactions with data (e.g., I thought unemployment was down this year) which influence their interpretations. Most design and evaluation techniques do not account for these influences. I'll describe what we've learned by developing and studying visualization interfaces that encourage reflecting on data in light of one's own or others' prior knowledge. I'll conclude by reflecting on how better representations of uncertainty and prior knowledge can contribute to a Bayesian model of visualization interpretation.
Bio: Jessica Hullman is an Assistant Professor in Computer Science and Journalism at Northwestern. The goal of her research is to develop computational tools that improve how people reason with data. She is particularly inspired by how science and data are presented to non-expert audiences in data and science journalism, where a shift toward digital news provides opportunities for informing through interactivity and visualization. Her work has provided automated tools and empirical findings around the use of visualizations to support communication and reasoning. Her current research focuses on how understandable presentations of uncertainty and interactive visualizations that enable users to articulate and reason with prior beliefs can transform how lay people and analysts alike interact with data.
Jessica has received numerous paper awards from top Visualization and HCI venues, and is the recipient of an NSF CRII and CAREER Awards among other grants. Prior to joining Northwestern in 2018, she spent three years as an Assistant Professor at the University of Washington Information School. She completed her Ph.D. at the University of Michigan and spent a year as the inaugural Tableau Software Postdoctoral Scholar in Computer Science at the University of California Berkeley in 2014 prior to joining the University of Washington in 2015.
Graphics & Vision, Human-Computer Interaction
Big Data, Education
Created by Amy Shea-Slattery at Thursday, October 04, 2018 at 11:31 AM.