Countertenor and conductor Tim Keeler is sought after as a performer, a conductor, and an educator. He is currently based in San Francisco and sings with the internationally acclaimed and Grammy award-winning vocal ensemble Chanticleer.
Prior to moving to the west coast, Tim was a member of the Grammy-nominated Choir of Trinity Wall Street and sang as a frequent soloist throughout New York City. He performed weekly Bach cantatas with Bach Vespers at Holy Trinity Lutheran Church, appeared regularly with TENET, New York's preeminent early music ensemble, and recorded and performed with the Grammy-nominated vocal quartet New York Polyphony. Also an avid proponent of new and challenging repertoire, Tim was a core member of Ekmeles, a vocal ensemble based in New York City and dedicated to contemporary, avant-garde, and infrequently-performed vocal repertoire.
During his time in New York, Tim also served as artistic director of Trident Ensemble, a new all-male vocal septet dedicated to novel and far-reaching programming. Repertoire included traditional Georgian music, works by 20th century Italian avant-garde composers, Estonian folk songs, and rare Franco-Flemish masterpieces.
As an educator, Tim directed the choirs at the Special Music School High School in Manhattan and worked closely with the Young People's Chorus of New York City as a vocal coach and satellite school conductor. He was also the choral conductor for Juilliard's new Summer Performing Arts program - a two-week intensive summer course in Geneva, Switzerland.
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.