Tim Keeler is music director of the San Francisco-based, GRAMMY® award-winning vocal ensemble Chanticleer. Performing nearly 100 concerts every year all over the world, Chanticleer has been a staple of the American choral sound for over 40 years.
Prior to moving to San Francisco, Tim forged a career as an active conductor, singer, and educator. He has performed with New York Polyphony, The Clarion Choir, the Choir of Trinity Wall Street, and sang with Chanticleer for their 2017-2018 season. He has also performed frequently as a soloist, appearing regularly in the Bach Vespers series at Holy Trinity Lutheran Church in New York City, as well as with TENET, New York's preeminent early music ensemble. An avid proponent of new and challenging repertoire, Tim remains a core member of Ekmeles, a vocal ensemble based in New York City and dedicated to contemporary, avant-garde, and infrequently-performed vocal repertoire.
While transitioning to his role as music director of Chanticleer, Tim is in the midst of completing his DMA in Choral Conducting at the University of Maryland where he studies with Dr. Edward Maclary. As an educator, Tim has directed the Men’s Chorus at the University of Maryland, served as director of 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 a BA 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 with Dr. Jerry Blackstone at the University of Michigan, Tim 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.