Sara Di Bartolomeo
PhD Student
Education
- MS in Computer Science and Engineering, La Sapienza, Rome – Italy
- BS in Computer Science and Engineering, La Sapienza, Rome – Italy
Biography
Sara Di Bartolomeo is a doctoral student studying data visualization at Northeastern University's Khoury College of Computer Sciences, advised by Cody Dunne. Her favorite aspect of data visualization is that it gives a scientific purpose to art and creativity and makes complex data accessible.
Before joining Northeastern, she earned her undergraduate and graduate degrees in computer science and engineering from La Sapienza in Italy. Sara is interested in making complex data more accessible and understandable, and in bridging the gap between creativity and technology. Through her research, she wants to provide the opportunity to share information in an easier and more communicative way and to open up more points of view in existing data.
Recent Publications
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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. -
Evaluating the Effect of Timeline Shape on Visualization Task Performance
Citation: Sara Di Bartolomeo, Aditeya Pandey, Aristotelis Leventidis, David Saffo, Uzma Haque Syeda, Elin Carstensdottir, Magy Seif El-Nasr, Michelle A. Borkin, and Cody Dunne. 2020. Evaluating the Effect of Timeline Shape on Visualization Task Performance. Proceedings of the 2020 CHI Conference on Human Factors in Computing Systems. Association for Computing Machinery, New York, NY, USA, 1–12. DOI:https://doi.org/10.1145/3313831.3376237 -
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.