Cody Dunne
Associate Professor
Research interests
- Information visualization
- Visual analytics
- Graph and network visualization
- Data workflows
- Data and analytic provenance
- Human–computer interaction
- Personal health informatics
Education
- PhD in Computer Science, University of Maryland
- MS in Computer Science, University of Maryland
- BA in Computer Science and Mathematics, Cornell College
Biography
Cody Dunne is an associate professor in the Khoury College of Computer Sciences at Northeastern University, based in Boston.
Dunne works at the intersection of information visualization, network science, and human–computer interaction. He focuses on techniques for making data easier to analyze and share, as well as the application of visualization techniques to real-world problems. He is currently working to improve the readability of network visualizations and to develop the next generation of tools for visually exploring, sharing, and collaborating around data and analysis workflows. Dunne has also delved into the visualization of concepts from medical records, infectious disease spread, academic literature citations, interactions of people and organizations, relationships in archaeological dig sites, news term co-occurrences, thesaurus category relationships, municipal energy use, and computer network traffic flow.
Before joining Khoury College, Dunne was a research scientist at IBM Watson Health, IBM Watson, and IBM Research.
Recent publications
-
Evaluating Graph Layout Algorithms: A Systematic Review of Methods and Best Practices
Citation: Sara Di Bartolomeo, Tarik Crnovrsanin, David Saffo, Eduardo Puerta, Connor Wilson, Cody Dunne. (2024). Evaluating Graph Layout Algorithms: A Systematic Review of Methods and Best Practices Comput. Graph. Forum, 43. https://doi.org/10.1111/cgf.15073 -
Investigating the Visual Utility of Differentially Private Scatterplots
Citation: Liudas Panavas, Tarik Crnovrsanin, Jane Lydia Adams, Jonathan R. Ullman, Ali Sarvghad, Melanie Tory, Cody Dunne. (2024). Investigating the Visual Utility of Differentially Private Scatterplots IEEE Trans. Vis. Comput. Graph., 30, 5370-5385. https://doi.org/10.1109/TVCG.2023.3292391 -
Six methods for transforming layered hypergraphs to apply layered graph layout algorithms
Citation: Sara Di Bartolomeo, Alexis Pister, Paolo Buono, Catherine Plaisant, Cody Dunne, Jean-Daniel Fekete. (2022). Six methods for transforming layered hypergraphs to apply layered graph layout algorithms Comput. Graph. Forum, 41, 259-270. https://doi.org/10.1111/cgf.14538 -
STRATISFIMAL LAYOUT: A modular optimization model for laying out layered node-link network visualizations
Citation: S. di Bartolomeo, M. Riedewald, W. Gatterbauer and C. Dunne, "STRATISFIMAL LAYOUT: A modular optimization model for laying out layered node-link network visualizations," in IEEE Transactions on Visualization and Computer Graphics, vol. 28, no. 1, pp. 324-334, Jan. 2022, DOI: 10.1109/TVCG.2021.3114756. -
Sequence Braiding: Visual Overviews of Temporal Event Sequences and Attributes
Citation: S. D. Bartolomeo, Y. Zhang, F. Sheng and C. Dunne, "Sequence Braiding: Visual Overviews of Temporal Event Sequences and Attributes," in IEEE Transactions on Visualization and Computer Graphics, vol. 27, no. 2, pp. 1353-1363, Feb. 2021, doi: 10.1109/TVCG.2020.3030442. -
Remote and Collaborative Virtual Reality Experiments via Social VR Platforms
Citation: David Saffo, Sara Di Bartolomeo, Caglar Yildirim, and Cody Dunne. 2021. Remote and Collaborative Virtual Reality Experiments via Social VR Platforms. In Proceedings of the 2021 CHI Conference on Human Factors in Computing Systems (CHI '21). Association for Computing Machinery, New York, NY, USA, Article 523, 1–15. DOI:https://doi.org/10.1145/3411764.3445426 -
QueryVis: Logic-based diagrams help users understand complicated SQL queries faster
Citation: Aristotelis Leventidis, Jiahui Zhang, Cody Dunne, Wolfgang Gatterbauer, H. V. Jagadish, and Mirek Ridewald. “QueryVis: Logic-based diagrams help users understand complicated SQL queries faster”. In: Proc. 2020 ACM SIGMOD International Conference on Management of Data. SIGMOD. Preprint & supplemental material: osf.io/btszh. SIGMOD 2021 Most Reproducible Paper Award. 2020, pp. 2303–2318. doi: 10.1145/3318464.3389767.
Related news
Current PhD students
Previous PhD students
-
Sara Di Bartolomeo
-
Justin Raynor
-
David Saffo