Saeed Mosayyebpour
About Saeed Mosayyebpour
Saeed Mosayyebpour is a Machine Learning Signal Processing Principle Engineer at Synaptics Incorporated, where he leads the Audio DSP AI team. He holds a PhD in Electrical and Computer Engineering from the University of Victoria and has extensive experience in the field, including previous roles at Malaspina Labs Inc. and Conexant.
Current Role at Synaptics
Saeed Mosayyebpour serves as a Machine Learning Signal Processing Principle Engineer at Synaptics Incorporated. He has held this position since 2017, contributing to the development of advanced technologies in the field of machine learning and signal processing. He leads the Audio DSP AI team, focusing on integrating artificial intelligence into digital signal processing applications. His work is based in Irvine, California, where he has been instrumental in driving innovation within the company.
Education and Expertise
Saeed Mosayyebpour earned his PhD in Electrical and Computer Engineering from the University of Victoria, where he studied from 2011 to 2014. Prior to this, he obtained a Bachelor's Degree in Electrical Engineering and a Master's Degree in Electrical and Electronics Engineering from Amirkabir University of Technology - Tehran Polytechnic, completing his undergraduate studies from 2003 to 2007 and his master's from 2007 to 2010. His educational background provides a strong foundation for his expertise in machine learning and signal processing.
Previous Experience
Before joining Synaptics, Saeed Mosayyebpour worked at Conexant as a DSP Senior Staff Engineer from 2015 to 2017, where he focused on digital signal processing technologies. He also served as a Senior Speech Scientist at Malaspina Labs Inc. for nine months in 2014 to 2015. His diverse experience in various roles has contributed to his proficiency in the field.
Patents and Publications
Saeed Mosayyebpour has authored nine patents as the first author within the first two years of his industry career. Additionally, he published three IEEE transactions during the initial two years of his PhD studies. These contributions highlight his active engagement in research and innovation within the fields of machine learning and signal processing.