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Julie Andreyev

Julie Andreyev
Born 1962
Burnaby, British Columbia
Education Diploma of Fine Arts, Emily Carr University of Art + Design, Master of Arts, Simon Fraser University
Known for Multidisciplinary art
Notable work Animal Lover, Vegan Congress, Scratch Theremin, *glisten) HIVE, Rockstar

Julie Andreyev (born 1962) is a Vancouver-based multidisciplinary artist whose practice explores themes of animal agency and consciousness. Her ongoing Animal Lover work explores nonhuman animal agency and creativity through modes of interspecies collaboration and aleatoric methods. The Animal Lover projects seek to contribute towards an ethic of compassion and regard for the intrinsic worth of other-than-human individuals. She was born in Burnaby, British Columbia.

Andreyev received her Diploma of Fine Arts degree from the Emily Carr University of Art + Design in 1988. She received a Master of Arts degree from the Simon Fraser University in 1999. She is currently a PhD candidate in Graduate Liberal Studies, Simon Fraser University where her research examines compassion as a means for improved human awareness and relationship with more-than-human worlds.

Julie Andreyev is currently an Associate Professor at the Emily Carr University of Art + Design, and Artistic Director of Interactive Futures.

Her work has been shown across Canada, in the US, Europe and Japan in galleries and festivals such as the Peabody Essex Museum, the Vancouver Art Gallery, SIGGRAPH, Vancouver 2010 Cultural Olympiad, Viper, CHI, Japan Media Arts Festival, Digital Art Weeks, and Nuit Blanche.

Andreyev's art is concerned with issues surrounding new media, social media, technology and human/nonhuman relationships. The Animal Lover projects are often produced in collaboration with companion dogs Tom and Sugi where respect, fun and challenge are employed in the process. The dogs participate directly in the research and content creation of the work by suggesting ideas for projects and by determining the material for production. Also, aleatoric and improvisational methods are used that provide for a chance, open-ended expectation, surprise and learning.


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