Eric Ragan

About

Eric Ragan is an Associate Professor at the University of Florida in the Department of Computer & Information Science & Engineering (CISE). He leads the Indie (Interactive Data and Immersive Environments) research lab, which conducts research in human-computer interaction (HCI), human-centered computing (HCC), information visualization, virtual reality, 3D interaction, visual analytics, and trust in intelligent systems. The Indie Lab focuses on the design and evaluation of applications and techniques that support effective interaction and understanding of data, information, and virtual environments. Their research also includes explainable AI, and the group involves undergraduate and graduate students from multiple departments, collaborating actively with faculty across the university. Prior to his current position, Eric Ragan was an assistant professor at Texas A&M University in the Department of Visualization and the Department of Computer Science & Engineering. Before entering academia, he worked as a visual analytics research scientist at Oak Ridge National Laboratory as part of the Situation Awareness and Visual Analytics research team. He earned his Ph.D. in Computer Science from Virginia Tech. His work centers on human-centered research of interactive visualizations and immersive environments, contributing to advancing understanding and interaction with complex data and virtual spaces.

Research topics

• Computer Science
• Cognitive science
• World Wide Web
• Cognitive psychology
• Human–computer interaction
• Psychology
• Mathematics

Selected publications

• Anchoring Bias Affects Mental Model Formation and User Reliance in Explainable AI Systems

  2021 · 95 citations

  EXplainable Artificial Intelligence (XAI) approaches are used to bring transparency to machine learning and artificial intelligence models, and hence, improve the decision-making process for their end-users. While these methods aim to improve human understanding and their mental models, cognitive biases can still influence a user’s mental model and decision-making in ways that system designers do not anticipate. This paper presents research on cognitive biases due to ordering effects in intellig…

  DOI

• An immersive virtual reality experimental study of occupants’ behavioral compliance during indoor evacuations

  International Journal of Disaster Risk Reduction · 2024-04-03 · 21 citations

  articleSenior author
  PublisherDOI

• Effects of a Block-Based Scaffolded Tool on Students’ Introduction to Hierarchical Data Structures

  IEEE Transactions on Education · 2021 · 11 citations

  Senior authorCorresponding

  <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Contribution:</i> In this article, the authors present findings and insights on the efficacy of using an educational block-based programming (BBP) environment—Blocks4DS, to teach the binary search tree (BST). <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">Background:</i> For a decade, BBP environments have been a hot topic in the computer science ed…

  PublisherDOI

• Examining Effects of Technique Awareness on the Detection of Remapped Hands in Virtual Reality

  IEEE Transactions on Visualization and Computer Graphics · 2024-03-04 · 10 citations

  articleSenior author

  Input remapping techniques have been widely explored to allow users in virtual reality to exceed both their own physical abilities, the limitations of physical space, or to facilitate interactions with real-world objects. Often considered is how these techniques can be applied to achieve maximum utility, but still be undetectable to users to maintain a sense of immersion and presence. Existing psychophysical methods used to determine these detection thresholds have known limitations: they are hi…

  PublisherDOI

• User Profiling in Human-AI Design: An Empirical Case Study of Anchoring Bias, Individual Differences, and AI Attitudes

  Proceedings of the AAAI Conference on Human Computation and Crowdsourcing · 2024-10-14 · 4 citations

  articleOpen accessSenior author

  People form perceptions and interpretations of AI through external sources prior to their interaction with new technology. For example, shared anecdotes and media stories influence prior beliefs that may or may not accurately represent the true nature of AI systems. We hypothesize people's prior perceptions and beliefs will affect human-AI interactions and usage behaviors when using new applications. This paper presents a user experiment to explore the interplay between user's pre-existing belie…

  PublisherOA PDFDOI

Recent grants

• III: Medium: Collaborative Research: Towards Effective Interpretation of Deep Learning: Prediction, Representation, Modeling and Utilization

  NSF · $216k · 2019–2024

• CRII: III: Evaluating Provenance Visualizations for the Presentation and Communication of Investigative Data Analysis Processes

  NSF · $81k · 2018–2020

• CRII: III: Evaluating Provenance Visualizations for the Presentation and Communication of Investigative Data Analysis Processes

  NSF · $181k · 2016–2019

Frequent coauthors

• Doug A. Bowman

  26 shared

• Sina Mohseni

  Nvidia (United States)

  21 shared

• Mahsan Nourani

  Universidad del Noreste

  20 shared

• E. Świerczyński

  Nicolaus Copernicus University

  18 shared

• M. Mikołajewski

  Fraunhofer Institute for Industrial Engineering

  16 shared

• C. Gałan

  16 shared

• T. Tomov

  Alexandrovska Hospital

  16 shared

• P. Wychudzki

  Nicolaus Copernicus University

  16 shared

Similar researchers at University of Florida

• Yuanyuan Leih-61
• Sanjay Rankah-50
• Shigang Chenh-50