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Lena Cibulski,
Fraunhofer IGD

Making Choices with Visualizations

Date: 18.01.2022, 13:00 CET
Room: Zoom/Youtube, opens an external URL in a new window/S3055

About the Speaker:
Lena Cibulski is a visualization researcher at Fraunhofer IGD and a PhD candidate at Technical University of Darmstadt, Germany. She received her master’s degree in computer science in 2017 from Otto-von-Guericke University Magdeburg, where she soon found her way into visualization research. She completed her bachelor studies with a six-month stay at the VRVis Research Center in Vienna. In early 2023, Lena will be a visiting researcher at JKU Linz for two months. Her research is at the intersection between visualization and multi-attribute decision-making, with an emphasis on design studies for engineering applications. She is particularly interested in multidisciplinary collaborations to encourage discussions on human factors, methodological aspects, and applications. Lena conducts industrial and research projects that aim at assisting and informing decisions by using interactive visualization. She has authored a number of journal and conference publications and regularly acts as a reviewer for conferences like IEEE VIS and EuroVis.

This talk provides different perspectives on using data visualization to assist and inform choices. We face many choices in our personal and professional lives. Computing has made it easy to compile large numbers of options to choose from. Identifying the best solution among such a set is called multi-attribute choice. With no objectively optimal solution present, our human judgment is needed to trade off conflicting goals.
Data visualization is a powerful tool to help us explore and make sense of available courses of action. While many interactive visualizations already live in the context of decision-making, how to design for humans who make decisions with visualized data continues to be a vibrant research area. In this talk, I will outline several properties of multi-attribute choices that we encountered when studying real users and data. I will also hint at how disciplines like decision theory might help with that. Finally, I will layout some open visualization challenges along with two examples, where our visualizations helped engineers learn what level of performance is achievable under which conditions, even for co-dependent choices.

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