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Alex Endert: Guest Speaker, Friday November 18, 2022

November 7, 2022

Alex Endert, VT/CS/CHCI alumnus, will visit Blacksburg on Friday, November 18 and give a talk on “Guidance during Visual Data Analysis: Enhancing Analysis and Combating Cognitive Bias” (1:00-2:00 pm, Multipurpose Room, Newman Library).

Alex Endert
Alex Endert is an Associate Professor in the School of Interactive Computing at Georgia Tech. He directs the Visual Analytics Lab, where he and his students design and study how interactive visual tools help people make sense of data and AI. His lab often tests these advances in domains including intelligence analysis, cyber security, decision-making, manufacturing safety, among other domains. His lab receives generous support from sponsors including NSF, DOD, DHS, DARPA, DOE, and industry. In 2018, he received a CAREER award from the National Science Foundation for his work on visual analytics by demonstration. He received his Ph.D. in Computer Science from Virginia Tech in 2012 (Chris North, advisor). In 2013, his work on Semantic Interaction was awarded the IEEE VGTC VPG Pioneers Group Doctoral Dissertation Award, and the Virginia Tech Computer Science Best Dissertation Award.

Abstract

Visual analytic tools emphasize the importance of combining interactive visualizations with data analytic models to give people insight into data. Through user interactions with these systems, people prepare data, explore and analyze it, and make decisions. These human-in-the-loop approaches tactfully combine the expertise of people with computational approaches. 

Often, various computational or AI models guide users throughout their exploration, and people provide feedback to these models as analysis proceeds. However, the cognitive sciences tell us that people also have innate cognitive biases that may influence this process. Thus, how can we design and build visual analytic tools that consider and guard against the possibility of biased analytic behavior? 

This talk will discuss the opportunities and challenges of guidance during visual data analysis, and give examples of how biased behavior can be detected and potentially mitigated.