Atabak Pouya
About Atabak Pouya
Atabak Pouya is an Audio Machine Learning Engineer at Synaptics Incorporated, specializing in machine learning applications for digital signals. He holds a Master of Science in Computational and Data Science and has experience in various engineering roles across multiple companies.
Current Role as Audio Machine Learning Engineer
Atabak Pouya currently works at Synaptics Incorporated as an Audio Machine Learning Engineer. He has held this position since 2021 and is based in Irvine, California. In this role, he focuses on applying machine learning techniques to audio processing, leveraging his expertise in deep learning algorithms.
Previous Experience at Synaptics
Atabak Pouya has a history of working at Synaptics Incorporated, where he initially served as a DSP Engineering Intern for 7 months in 2019. Following this internship, he transitioned to the role of DSP Engineer, where he worked for 2 years until 2021. His experience at Synaptics has contributed to his skills in digital signal processing.
Educational Background
Atabak Pouya holds a Master of Science degree in Computational and Data Science from Chapman University, which he completed from 2017 to 2019. He also studied Embedded Systems Development at the Institute of Technology Development of Canada from 2016 to 2017. Earlier, he earned a Bachelor of Science degree in Electrical Electronic Engineering from the University of Tabriz, graduating in 2014.
Technical Skills and Expertise
Atabak Pouya possesses strong programming skills in languages such as Python, MATLAB, C++, and LaTeX. He is proficient in using computational frameworks like TensorFlow and Keras, which are essential for machine learning and deep learning applications. His expertise includes applying these technologies to analyze digital signals, including EEG, EMG, and ECG.
Research Interests and Projects
Atabak Pouya has a research interest in Natural Language Processing. He is involved in various projects that explore the intersection of audio processing and machine learning, contributing to advancements in the field. His work reflects a commitment to integrating machine learning with practical applications in audio technology.