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Data visualizations are designed to leverage the impressive human visual bandwidth so that users can spot clusters, gaps, trends, outliers in the data within a fraction of a second. Visualizations, combined with interaction and animation techniques, can help experts interpret and explore complex data as well as gain confidence in their algorithmic results. Moreover, visualizations are highly effective tools for communicating with other analysts or stakeholders. Research on visualization involves understanding human perception and vision, visual encodings, design thinking, color choice, data simplification, interaction techniques, and animation techniques and designing the next generation of data analysis and communication tools. We have several current research opportunities available for faculty, research scientists, postdoctoral researchers, and students joining our PhD, master's, and bachelor's programs.

Areas of investigation:

  • Perception and cognition
  • Data storytelling
  • Analytic provenance
  • Exploratory data analysis
  • Coordinated views and interactions
  • Tree/hierarchical data
  • Network data
  • Multidimensional data
  • Geospatial data
  • Uncertain, missing, erroneous data
  • Temporal event sequences
  • User interface design
  • Interaction design
  • Evaluation methodologies

Specific domains of interest:

  • Healthcare diagnostic and treatment decision support, including diabetes, neurology, ophthalmology
  • Cybersecurity, including unmanned autonomous system analysis
  • Astronomy and physics, including 3D visualization and multidimensional data
  • Computer Science, including work in databases, programming languages, and systems
  • Digital humanities, including networks of concepts in humanities texts, text and timeline visualizations
  • Epidemiology, including the spread of infectious disease

Updates

2022-09-28: Professor John Alexis Guerra Gomez will be giving an invited talk to Bogota's Major's office on the importance of clean data for visual analytics.
2022-04-15: Welcome to our new PhD students Racquel Fygenson, Eduardo Puerta, Connor Wilson, and Sydney Purdue!
2022-04-08: 38 authors from Northeastern were represented at CHI 2022 and there were 11 contributions from Khoury! Our group contributed two different papers, a late-breaking work, and a case study.
2022-04-06: Our PhD student Sara Di Bartolomeo was recognized by the university as a Huntington 100 scholar for her outstanding academic achievements!
2022-03-25: Three papers from our group will appear in EuroVis 2022, plus a keynote by Melanie Tory!

Recent Publications

Thumbnail image for publication titled: Six methods for transforming layered hypergraphs to apply layered graph layout algorithms
Six methods for transforming layered hypergraphs to apply layered graph layout algorithms

Sara Di Bartolomeo, Alexis Pister, Paolo Buono, Catherine Plaisant, Cody Dunne, and Jean-Daniel Fekete. Computer Graphics Forum—EuroVis/CGF. 2022

PDF | DOI | Supplement | BibTeX

Thumbnail image for publication titled: DyStopia: Into a potential future of IEEE VIS under Plan S
DyStopia: Into a potential future of IEEE VIS under Plan S

Lonni Besançon and Cody Dunne. Proc. alt.VIS workshop at IEEE VIS—alt.VIS. 2022

PDF | Preprint | DOI | Supplement | Teaser Observable | Award | BibTeX | alt.VIS Most Dystopian award!

Thumbnail image for publication titled: The worst graph layout algorithm ever
The worst graph layout algorithm ever

Sara Di Bartolomeo, Matěj Lang, and Cody Dunne. Proc. alt.VIS workshop at IEEE VIS—alt.VIS. 2022

PDF | Preprint | DOI | Supplement | Award | BibTeX | alt.VIS The Worst (Algorithm) award!

Thumbnail image for publication titled: Effect of anthropomorphic glyph design on the accuracy of categorization tasks
Effect of anthropomorphic glyph design on the accuracy of categorization tasks

Aditeya Pandey, Peter Bex, and Michelle A. Borkin. In Proc. Extended Abstracts of CHI Conference on Human Factors in Computing Systems—CHI EA. 2022

PDF | Preprint | DOI | Supplement | BibTeX

Thumbnail image for publication titled: GenoREC: A recommendation system for interactive genomics data visualization
GenoREC: A recommendation system for interactive genomics data visualization

Aditeya Pandey, Sehi L'Yi, Qianwen Wang, Michelle A. Borkin, and Nils Gehlenborg. IEEE Transactions on Visualization and Computer Graphics—VIS/TVCG. 2022

PDF | Preprint | DOI | Supplement | BibTeX | Poster at VIS 2020 won the Best Poster Award!


Data Visualization @ Khoury — Northeastern University
West Village H, Room 302
440 Huntington Ave, Boston, MA 02115, USA