Born and raised in Montréal (Québec), I received my bachelor's degree ('09) in Electrical Engineering from McGill University, and my Master's degree ('11) and Ph.D. degree ('17) in Electrical Engineering from the Université de Sherbrooke. I then worked as a Postdoctoral Associate at Massachusetts Institute of Technology in CSAIL for two years, and I'm currently an assistant professor in the Department of Electrical Engineering and Computer Engineering at Université de Sherbrooke since 2020.

I'm a member of IntRoLab at the Interdisciplinary Institute for Technological Innovation. I am also a member of INTER, CRASH and CdRV. My current research mainly focuses on robot audition. This is an exciting field as the end goal is to allow natural human-robot interaction with voice in everyday life environments. This task involves numerous challenges, that go beyond traditional far field speech recognition approaches. In addition to dealing with room reverberation, intefering noise and/or competing speakers, robots must ignore the noise generated by their own actuators, which we refer to as ego-noise, and perform online recognition with little latency. Whilst deep learning-based approaches have been successful in improving speech recognition robustness, they rely on large amount of data and cloud computing. This is a major challenge in robotics, as the amount of experimental data is limited (each robot is unique!), and autonomous robots must perform computations on-board. My current research articulates around four main themes: 1) sound source localization, 2) speech enhancement, 3) sound classification and 4) ego-noise reduction.