Teaching at the college level since 1994 and in engineering since 2004, and previously an associate professor of EE at Oregon Tech, Vurkaç earned his Ph.D. in Electrical and Computer Engineering in December 2011 at Portland State University with research at the confluence of machine learning, information theory, and mathematical music theory. His current research areas are transfer learning and evolutionary computation in image processing, music perception in computational neuroscience, ensemble learning, engineering education, multivariate modeling in mathematical music theory, and music information retrieval.

Music Information Research abounds with work on the recognition of style, genre, composer and singer, and the detection of beat, tempo, metre and even emotion. However, some musical attributes remain unexplored. One of these, clave direction, is a rhythmic principle found throughout Latin American music. The purpose of this article is to introduce the significance and potential pedagogical role of automated clave-direction identification, to propose a system-level implementation and to suggest application areas that may benefit performers, educators, students and industry. Clave direction is an inherent feature of rhythmic patterns, not just of the standard sequences commonly associated with clave. Technological aids to composition, arranging, education, search and music production are needed for a growing population of musicians. Existing and developing methods of music technology can be used to fulfil this need. Target applications include rhythmtraining equipment, recording and sequencing software, auto-accompaniment and automated querying of databases or the Internet. Mehmet Vurkaç 28

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Mehmet Vurkaç is an associate professor of Electrical Engineering and Renewable Energy (EERE) at Oregon Institute of Technology, where he has also taught courses in critical thinking, percussion, and mathematics.Vurkaç is on sabbatical at Seattle University, in the department of Electrical and Computer Engineering, for the academic year 2016-'17.Vurkaç earned his Ph.D. in Electrical and Computer Engineering in December 2011 at Portland State University, with research at the confluence of machine learning, information theory, philosophy of science, music information retrieval, and mathematical music theory. His current research areas are engineering education, music information retrieval (DSP and machine learning), music perception, and mathematical music theory.Prior to tenure track (1994 through 2010), Vurkaç taught in the following academic settings.