“The Connective Tissue Series” Improvisational Movement Journey with Ashley Menestrina


May 09 2020


1:00 pm - 2:00 pm




$10 via www.paypal.me/pizarts or $33 monthly unlimited classes

“The Connective Tissue Series” is an improvisational journey that will allow us to put into perspective the relationship we have with ourselves and our surroundings. From the physical tissue that connects our bodies to the more metaphorical connections that allow us to be seen and heard in every day life, this workshop series will be full of questions and insightful investigations. Pertaining to the topic of connectivity, each weekend we will explore a different dimension of this theme. Participants are free to join for one or both sessions



BIO: Ashley Menestrina is a freelance artist performing her own work around the world. Additionally, she freelances for Shawn Bible Dance Company in NYC. Her self-choreographed solo, “Always a Creature” has been performed at the David Rubenstein Atrium at Lincoln Center, the CURRENT SESSIONS’, Peridance’s Salvatore Capezio Theater, Center for Performance Research, and the 14th Street Y. Her second solo work, “The Human Condition: Absent Presence” premiered at The Martha Graham Theater in April 2018 and has since toured in New York City, Los Angeles, Turkey, Mexico City, Portugal, and Israel, taking home 3rd place at the International Choreography Competition in Jerusalem as part of Jerusalem International Dance Week. Her newest work, “Combative Echoes”, was originally shown in Thessaloniki, Greece for Die Wolke Art Group’s Unit Motives: GRM festival in 2019, and has since been extended to premier in Germany at the 24th International Solo-Dance-Thetare Festival Stuttgart in 2020. Ashley’s choreography has been set on the students at Manhattanville College as well as various studios across the nation. She currently teaches various workshops in and around the New York City area.

Class information is gathered through community submissions. Dancing Alone Together takes no responsibility for class quality or errors in event data.