Tim Keeler

Credit: Clayton Raithel

Conductor and countertenor Tim Keeler is sought after as both a performer and an educator. He is a member of the Choir of Trinity Wall Street and sings as a frequent soloist throughout New York City. He performs weekly Bach cantatas with Bach Vespers at Holy Trinity Lutheran Church and appears regularly with TENET, New York’s preeminent early music ensemble. He has recorded with the Grammy-nominated vocal quartet New York Polyphony and will make his Carnegie Hall debut in February 2017 with the British early music ensemble, Gallicantus. Also an avid proponent of new and challenging repertoire, Tim is a core member of Ekmeles, a vocal ensemble based in New York City dedicated to contemporary, avant-garde and infrequently-performed vocal repertoire.

Tim is the artistic director of Trident Ensemble, a new all-male vocal ensemble dedicated to novel and perceptive programming. Their first season of well-received concerts included the US premiere of Shostakovich’s Loyalty, a reflective and searching work for male choir written on the 100th anniversary of Vladimir Lenin’s death.

As an educator, Tim is in demand both in New York and around the world. He conducts the choirs at the Special Music School High School in Manhattan and works closely with the Young People’s Chorus of New York City as a vocal coach and satellite school conductor. He is also the choral conductor for Juilliard’s new Summer Performing Arts Program, an international summer course collaboration with Nord Anglia Education.

Tim holds an AB in Music from Princeton University with certificates in Vocal Performance and Computer Science, an MPhil in Music and Science from Cambridge University, and an MM in Choral Conducting from the University of Michigan. While studying at the University of Michigan, Tim conducted numerous University ensembles and served as assistant conductor of the Grammy-award winning UMS Choral Union, preparing the choir for performances with Leonard Slatkin and the Detroit Symphony Orchestra. His dissertation at Cambridge explored statistical methods used in natural language processing and unsupervised machine learning as applied to musical phrase detection and segmentation.