Alex Endert
Associate Professor
Associate Chair of Operations and Special Initiatives
School of Interactive Computing
Georgia Tech

85 5th St., NW
Technology Square Research Building, Office 335
Atlanta, GA 30332-0760

Office: TSRB 335
Tel: (404) 385-4477
endert@gatech.edu

Research Interests

My research helps people make sense of data and AI through the use of interactive visualizations, for the purpose of empowering individuals to gain insights into datasets, make informed decisions, and promote the responsible use of AI for domain-driven problems. My research group (Georgia Tech Visual Analytics Lab) approaches this challenge through combining techniques from visualization, human-computer interaction, and AI. Check out our projects, publications, and tools at our research group site.

	Detecting and Mitigating Cognitive Bias during Visual Data Analysis People are critical to human-in-the-loop visual analytics. Systems learn important domain expertise from people. However, how can we build systems that also alert people of potentially biased strategies?
	Guidance for Visual Data Analysis As data analysis tasks increase in complexity, people rely on hints to guide their tasks. We develop techniques to guide users in the process of gathering data and clearning data, performing visual data analysis, and presenting findings.
	Visualization for AI-Augmented Online Learning Visualization to support the NSF AI Institute (AI-ALOE) in showing teachers and learners how AIs are helping with various tasks in online learning environments.
	Enhancing Functional Vehicle Safety, Engineering, and Design Through design studies and close collaboration with domain experts from the automotive industry, we develop visualization tools to aid in the design and testing of functional safety components of cars.
	Visualization by Demonstration User interaction for visualization that lets people demonstrate intended visual mappings, data transformations, and visual representations to explore data.
	Building, Steering, and Comparing AI Models for Visual Data Analysis As the number of AI and ML models continues to increase, people often have the need to consider multiple different models for a given task. We build visualizations to help editing, tune, and adapt a variety of model types to better support user tasks, domains, and datasets.
	Analytic Provenance Data analysis consists of many steps, interactions, questions, and other activities to achieve insight. We explore methods for aiding people to capture, visualize, and leverage their analytic provenance during and after analysis.
	Visit our Visual Analytics Lab website for more!