Research

Tatiana Losev

I aim to bridge perspectives on data among people in the public, scientists, and technical experts. I use data visualization as a socially-oriented approach to include diverse views in research and design.

I am grateful for the many colleagues, mentors, and enthusiastic public community members who shape and inspire my work. They encourage me to push the boundaries of liminal and transdisciplinary spaces.

I earned my Ph.D. at the School of Interactive Arts & Technology at Simon Fraser University in Vancouver, Canada, with the mentorship of Dr. Sheelagh Carpendale. I'm part of the ixLab, InnoVis, and a visitor at the iLab and the Pain Studies Lab.

Addressing Health & Climate Crises With the Public

Scientific messages from experts to the public rely on data visualizations as a factual way to engage different people about COVID-19 or the ongoing climate crisis. I consider flipping the top-down science delivery model and exploring data visualization as a possible grassroots interactive social activity where data experts learn with and from people outside the lab.

I Use Data Visualization as a Qualitative Research and Design Approach

I facilitate cooperative research experiences in community involved data practices that foreground community agency, histories, strengths, and collective know-how. Merging qualitative design-oriented methodologies, I explore data visualization for social connection, sense-making, learning, and self-expression. For example, participants and collaborators use digital tools, pen-paper sketching, or familiar craft materials to build 3-D data representations called data physicalization.

Some of My Questions

How can data visualization and its process activities be used to facilitate and support social interactions among researchers and people in the public?

Can data visualizations be designed to promote a feeling of social connection among people?

What would an alternative social and visual lexicon to promote social and affective experiences with data visualizations look like?

My Google Scholar