[Ieee_vis_open_positions] ostDoc and Ph.D. Positions in Personalized VA open at University of Zurich

[Jürgen Bernard]

Dear colleagues,

The Interactive Visual Data Analysis (IVDA) Group at the University of 
Zurich (UZH) invites applications for both a Ph.D. and a Post-Doc track 
(m/f/d), at the intersection of visual analytics, interactive ML, and 
human-centered AI. Together, we will design and develop novel approaches 
for the characterization, design, and evaluation of interactive visual 
data analysis solutions that combine the strengths of both humans and 
algorithms.
We are pursuing a human-centered approach to data science, machine 
learning, and AI, to foster human involvement rather than replace it in 
the data analysis process. In the context of digitalization, data-driven 
research and decision-making, human-machine collaboration, and 
human-centered AI, we will study human workflows, identify and preserve 
steps with high human intellectuality, and establish practices of both 
high human control and automation.



Both positions will work in a project about human preference elicitation 
and multi-criteria decision support, which we will study for the case of 
item ranking challenges. The figure exemplifies a multi-criteria 
decision-making problem: buying the car, according to five elicited 
criteria. We will study how such highly personalized ranking approaches 
can be achieved interactively.

Title of the SNF-funded project is: Personalized Visual Analytics Human 
Preference Elicitation for Ranking-Based Multi-Criteria Decision Support.
Project duration is four years, in collaboration with Prof. Mennatallah 
El-Assady (ETH Zurich).
Start date is end of 2024.
Flyer: https://juergen-bernard.de/pdf/postDocFlyer.pdf
Job Announcement: at: https://www.ifi.uzh.ch/en/ivda/open-positions.html

For more information, please reach out me.
Best,
Jürgen Bernard

Research Context: Multi-criteria decision support through item ranking 
is an understudied research direction. Current solutions exhibit 
considerable limitations, primarily due to their inability to meet the 
multi-faceted nature of human decision-making. Choosing between 
thousands of relevant items is a common task, where people are left 
alone with the need to express and balance multiple desirable 
preferences at once. Item rankings are a popular and universal approach 
to structuring unorganized item collections by multiple criteria. With 
IVDA solutions, people can express multiple preferences, used by 
algorithms to compute human-centered and personal rankings. Visual 
Analytics (VA) is a field of research that supports complex human 
decision-making tasks by bringing together human intellectuality with 
the computational power of algorithms in effective human-in-the-loop 
approaches. The figure on the right exemplifies a multi-criteria 
decision-making problem: buying the car, according to five elicited 
criteria. We will study how such highly personalized ranking approaches 
can be achieved interactively.

-- 
Jürgen Bernard
Assistant Professor of Computer Science
University of Zurich
juergen-bernard.info
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