Choreography is by two graduate students, Kristin Dowdy and Zhongyuan Fa
Brockport, NY— The Department of Dance at The College at Brockport presents Challenging Convention: An MFA Thesis Concert on Friday and Saturday, February 15 and 16, 2019, at the Tower Fine Arts Center Black Box Theatre, 180 Holley Street, on the Brockport campus. Performances begin at 7:30 pm. Ticket prices for all performances are $17/General, $12/Seniors, College at Brockport Alumni, Faculty and Staff and $9/Students and are available online atfineartstix.brockport.edu, by phone at (585) 395-2787 or at the Tower Fine Arts Center Box Office, 180 Holley Street, Brockport.
“Spectrum” evolved from Kristin Dowdy’s written MFA thesis, “Gendered Gesture.” Within that work, Dowdy questioned and explored the social implications of gender and the impact that the gender binary has upon the physical body. In her dance, the audience will view an interpretation of how societal standards of gender manifest within the body. Dowdy attempts to strip away masculine and feminine constructs, so the dancers are free to liberate themselves from gendered expectations. “Spectrum” has been inspired by many prominent choreographers and their work with gender; by analyzing past choreographic works by Joe Goode, Ted Shawn, Mark Morris, and Tere O’Connor, Dowdy has employed strategies and tools to aid in construction of her work and to formulate her own vision and opinions about the gendered body.
The evening will open with “Unabridged Emotions,” Zhongyuan Fa’s thesis work. The piece is a mixed-reality dance performance that explores the expression of basic emotions through poetry, dance and facial expression. A lack of self-expression can lead to robotic movement and flat, lifeless dancing. This can translate to rather emotionless, blank facial expressions. In collaboration with students from the Rochester Institute of Technology, during the piece, facial avatars controlled by users off-stage will be projected on stage while each dancer portrays a particular basic emotion. These images will be generated using a network comprised of credit card-sized Raspberry Pi microcomputers. Integrated cameras will display facial expressions and communication via facial motion capture.