Enrico Bertini
Associate Professor, Jointly Appointed with College of Arts, Media and Design
Research interests
- Visualization
- Machine learning
Education
- PhD in Computer Engineering, University of Rome, La Sapienza — Italy
Biography
Enrico Bertini is an associate professor in the Khoury College of Computer Science and the College of Arts, Media and Design at Northeastern University, based in Boston.
Bertini works on data visualization interfaces to help people make sense of the world through data. In recent years, his work has focused on the use of visual interfaces to explore, validate, and understand machine learning models and systems. His research also aims to advance the theoretical and empirical understanding of how people extract information and meaning from visual representations.
Between 2006 and 2012, Bertini was a research scientist at the University of Fribourg in Switzerland and the University of Konstanz in Germany. In 2012, he joined the NYU School of Engineering as an assistant professor; he was promoted to the rank of associate professor in 2018. Bertini is the co-host of Data Stories, a popular podcast series that discusses the role of data in everyday life.
Labs and groups
Recent publications
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Cognitive Affordances in Visualization: Related Constructs, Design Factors, and Framework
Citation: Racquel Fygenson, Lace M. K. Padilla, Enrico Bertini. (2025). Cognitive Affordances in Visualization: Related Constructs, Design Factors, and Framework CoRR, abs/2509.09510. https://doi.org/10.48550/arXiv.2509.09510 -
PDPilot: Exploring Partial Dependence Plots Through Ranking, Filtering, and Clustering
Citation: Daniel Kerrigan, Brian Barr, Enrico Bertini. (2025). PDPilot: Exploring Partial Dependence Plots Through Ranking, Filtering, and Clustering IEEE Trans. Vis. Comput. Graph., 31, 7377-7390. https://doi.org/10.1109/TVCG.2025.3545025 -
Visual Exploration of Machine Learning Model Behavior With Hierarchical Surrogate Rule Sets
Citation: Jun Yuan, Brian Barr, Kyle Overton, Enrico Bertini. (2024). Visual Exploration of Machine Learning Model Behavior With Hierarchical Surrogate Rule Sets IEEE Trans. Vis. Comput. Graph., 30, 1470-1488. https://doi.org/10.1109/TVCG.2022.3219232 -
The Arrangement of Marks Impacts Afforded Messages: Ordering, Partitioning, Spacing, and Coloring in Bar Charts
Citation: Racquel Fygenson, Steven Franconeri, Enrico Bertini. (2023). The Arrangement of Marks Impacts Afforded Messages: Ordering, Partitioning, Spacing, and Coloring in Bar Charts CoRR, abs/2308.13321. https://doi.org/10.48550/arXiv.2308.13321 -
SliceLens: Guided Exploration of Machine Learning Datasets
Citation: Daniel Kerrigan, Enrico Bertini. (2023). SliceLens: Guided Exploration of Machine Learning Datasets HILDA@SIGMOD, 1:1-1:7. https://doi.org/10.1145/3597465.3605217 -
State of the Art of Visual Analytics for eXplainable Deep Learning
Citation: Biagio La Rosa, Graziano Blasilli, Romain Bourqui, David Auber, Giuseppe Santucci, Roberto Capobianco, Enrico Bertini, Romain Giot, Marco Angelini. (2023). State of the Art of Visual Analytics for eXplainable Deep Learning Comput. Graph. Forum, 42, 319-355. https://doi.org/10.1111/cgf.14733 -
Multiple Forecast Visualizations (MFVs): Trade-offs in Trust and Performance in Multiple COVID-19 Forecast Visualizations
Citation: Lace M. K. Padilla, Racquel Fygenson, Spencer C. Castro, Enrico Bertini. (2023). Multiple Forecast Visualizations (MFVs): Trade-offs in Trust and Performance in Multiple COVID-19 Forecast Visualizations IEEE Trans. Vis. Comput. Graph., 29, 12-22. https://doi.org/10.1109/TVCG.2022.3209457 -
SUBPLEX: A Visual Analytics Approach to Understand Local Model Explanations at the Subpopulation Level
Citation: Jun Yuan, Gromit Yeuk-Yin Chan, Brian Barr, Kyle Overton, Kim Rees, Luis Gustavo Nonato, Enrico Bertini, Cláudio T. Silva. (2022). SUBPLEX: A Visual Analytics Approach to Understand Local Model Explanations at the Subpopulation Level IEEE Computer Graphics and Applications, 42, 24-36. https://doi.org/10.1109/MCG.2022.3199727 -
Misinformed by Visualization: What Do We Learn From Misinformative Visualizations?
Citation: Leo Yu-Ho Lo, Ayush Gupta, Kento Shigyo, Aoyu Wu, Enrico Bertini, Huamin Qu. (2022). Misinformed by Visualization: What Do We Learn From Misinformative Visualizations? Comput. Graph. Forum, 41, 515-525. https://doi.org/10.1111/cgf.14559 -
The Exploratory Labeling Assistant: Mixed-Initiative Label Curation with Large Document Collections
Citation: Cristian Felix, Aritra Dasgupta, Enrico Bertini. (2018). The Exploratory Labeling Assistant: Mixed-Initiative Label Curation with Large Document Collections UIST, 153-164. https://doi.org/10.1145/3242587.3242596
Current PhD students
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Racquel Fygenson
PhD Student
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Daniel Kerrigan
PhD Student