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Computer Science

D-Index
34
Citations
4630
World Ranking
12220
National Ranking
4959

Research.com Recognitions

  • 2020 - ACM Senior Member

Overview

David Gotz is a researcher affiliated with the University of North Carolina at Chapel Hill in the United States. Their work primarily focuses on computer science, with specific emphasis on computer vision and pattern recognition as well as artificial intelligence. The research portfolio also includes contributions to general health professions, economics and econometrics, and public health, environmental and occupational health.

The main themes in David Gotz's research involve data visualization and analytics, data analysis with R, image retrieval and classification techniques, health systems including economic evaluations and quality of life, advanced text analysis techniques, video analysis, and summarization, as well as topics related to mental health research.

The scientist has published multiple papers spanning several years, notable recent publications include:

  • "Survey on Visual Analysis of Event Sequence Data" (2021), published in IEEE Transactions on Visualization and Computer Graphics
  • "AutoClips: An Automatic Approach to Video Generation from Data Facts" (2021), published in Computer Graphics Forum
  • "Visual Causality Analysis of Event Sequence Data" (2020), published in IEEE Transactions on Visualization and Computer Graphics
  • "Selection-Bias-Corrected Visualization via Dynamic Reweighting" (2020), published in IEEE Transactions on Visualization and Computer Graphics
  • "A rapidly deployed, interactive, online visualization system to support fatality management during the coronavirus disease 2019 (COVID-19) pandemic" (2020), published in Journal of the American Medical Informatics Association

David Gotz has collaborated frequently with several coauthors, including David Borland, Allison M. Deal, Antonia V. Bennett, Arran Zeyu Wang, and Ethan Basch. These collaborations have contributed to the development and dissemination of research in their fields of study.

The scientist's work has appeared repeatedly in certain academic venues, with the most frequent publication outlets including:

  • IEEE Transactions on Visualization and Computer Graphics
  • arXiv (Cornell University)
  • The Journal of Urology
  • UNC Libraries
  • Computer Graphics Forum

David Gotz was awarded the ACM Senior Member distinction in 2020, a recognition associated with the computing and information technology community.

Best Publications

  • Characterizing users' visual analytic activity for insight provenance

    David Gotz;Michelle X. Zhou

  • Behavior-driven visualization recommendation

    David Gotz;Zhen Wen

  • PixelFlex: a reconfigurable multi-projector display system

    Ruigang Yang;David Gotz;Justin Hensley;Herman Towles

  • Exploring Flow, Factors, and Outcomes of Temporal Event Sequences with the Outflow Visualization

    K. Wongsuphasawat;D. Gotz

  • Iterative Refinement of Cohorts Using Visual Exploration and Data Analytics

    David Gotz;Adam Perer;Zhiyuan Zhang

  • Progressive Visual Analytics: User-Driven Visual Exploration of In-Progress Analytics

    Charles D. Stolper;Adam Perer;David Gotz

  • DecisionFlow: Visual Analytics for High-Dimensional Temporal Event Sequence Data

    David Gotz;Harry Stavropoulos

  • FacetAtlas: Multifaceted Visualization for Rich Text Corpora

    Nan Cao;Jimeng Sun;Yu-Ru Lin;D Gotz

  • Visual analytics in healthcare – opportunities and research challenges

    Jesus J. Caban;David H Gotz

  • DICON: Interactive Visual Analysis of Multidimensional Clusters

    Nan Cao;D. Gotz;J. Sun;Huamin Qu

  • A methodology for interactive mining and visual analysis of clinical event patterns using electronic health record data

    David Gotz;Fei Wang;Adam Perer

  • A Survey on Visual Analytics of Social Media Data

    Yingcai Wu;Nan Cao;David Gotz;Yap-Peng Tan

  • Data-Driven Healthcare: Challenges and Opportunities for Interactive Visualization

    David Gotz;David Borland

  • A Survey on Visual Analysis of Event Sequence Data

    Yi Guo;Shunan Guo;Zhuochen Jin;Smiti Kaul

  • EventThread: Visual Summarization and Stage Analysis of Event Sequence Data

    Shunan Guo;Ke Xu;Rongwen Zhao;David Gotz

  • Predicting Patient's Trajectory of Physiological Data using Temporal Trends in Similar Patients: A System for Near-Term Prognostics.

    Shahram Ebadollahi;Jimeng Sun;David Gotz;Jianying Hu

  • Interactive Visual Synthesis of Analytic Knowledge

    D. Gotz;M.X. Zhou;V. Aggarwal

  • Characterizing users’ visual analytic activity for insight provenance

    D. Gotz;M.X. Zhou

  • Iterative cohort analysis and exploration

    Zhiyuan Zhang;David Gotz;Adam Perer

  • Methods for organizing information accessed through a web browser

    David Gotz

  • Adaptive Contextualization: Combating Bias During High-Dimensional Visualization and Data Selection

    David Gotz;Shun Sun;Nan Cao

  • Visual Progression Analysis of Event Sequence Data

    Shunan Guo;Zhuochen Jin;David Gotz;Fan Du

  • Health and Fitness Apps for Hands-Free Voice-Activated Assistants: Content Analysis.

    Arlene E Chung;Ashley C. Griffin;Dasha Selezneva;David H Gotz

  • Survey on Visual Analysis of Event Sequence Data

    Yi Guo;Shunan Guo;Zhuochen Jin;Smiti Kaul

Frequent Co-Authors

Nan Cao
Nan Cao Tongji University
Jimeng Sun
Jimeng Sun University of Illinois at Urbana-Champaign
Adam Perer
Adam Perer Carnegie Mellon University
Michelle X. Zhou
Michelle X. Zhou IBM (United States)
Yu-Ru Lin
Yu-Ru Lin University of Pittsburgh
Hongyuan Zha
Hongyuan Zha Chinese University of Hong Kong, Shenzhen
Huamin Qu
Huamin Qu Hong Kong University of Science and Technology
Baoxin Li
Baoxin Li Shaanxi Normal University
Yingcai Wu
Yingcai Wu Zhejiang University
Fatih Porikli
Fatih Porikli Australian National University

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