Summary
Pablo Zinemanas is a research engineer specializing in pattern recognition, machine learning, deep learning and audio signal processing, with nine years of experience spanning academia and industry. Currently at BMAT Music Innovators after R&D and senior audio AI roles at Novanta and Circulr, he bridges interpretable AI research from his PhD work with practical audio and music technology applications. He has a strong academic foundation as a former PhD student and teaching assistant at Universidad de la República and Universitat Pompeu Fabra, where he also explored automatic music transcription using image and audio techniques. Pablo combines hands-on hardware experience and product-minded engineering—having developed mobile peripherals and Android apps—with pedagogical strengths in didactics and course instruction. Based in Barcelona, he excels at turning signal-processing research into deployable systems for environmental sound and music analysis. An uncommon strength is his blend of cultural project leadership and technical depth, which helps him communicate complex AI ideas to diverse stakeholders.
9 years of coding experience
6 years of employment as a software developer
Doctor of Philosophy - PhD, Interpretable AI, PhD, Doctor of Philosophy - PhD, Interpretable AI, PhD at Universitat Pompeu Fabra
Master's degree, Electrical and Electronics Engineering, Master's degree, Electrical and Electronics Engineering at Universidad de la República
English, Portuguese, Spanish