The field of chart question answering (CQA) has begun to combine analyses of images and text in visualizations using recent advances in computer vision and natural language processing. Ideally, computational methods might mimic human chart perception, but how to best use computational approaches in ways that benefit from knowledge of human visual inference remains an open question, among many other unexplored research opportunities in CQA.

This workshop has three goals: (1) cognition researchers will introduce concepts of human chart perception to inspire algorithmic design, (2) computer scientists will present state-of-the-art machine learning techniques for chart analysis including respective limitations, (3) our proposed algorithmic challenge and call for submissions will spur the development of new CQA systems.


mid-February 2021: CQA Challenge Available!
June 15th 2021: Paper Submission Due
June 21st 2021: Challenge Submission Due
June 21st 2021: Electronic Poster Deadline
June 25th 2021: Workshop!


To be announced. The list of confirmed speakers is available here.


Daniel Haehn
University of Massachusetts Boston
Narges Mahyar
University of Massachusetts Amherst
Steven Franconeri
Northwestern University
Jessica Hullman
Northwestern University
Nikolaus Kriegeskorte
Columbia University
Hanspeter Pfister
Harvard University