Farnood Faraji

Farnood Faraji has been working in the field of Machine Learning and Data Science since 2017. In 2020, they joined AAVAA Inc. as a Machine Learning Engineer / Data Scientist, where they developed data processing pipelines for EEG, IMU and stimulus data to be efficiently stored and accessed through cloud or local devices. Farnood also analyzed brain EEG signals and performed feature extraction to build robust and efficient ML models using python, TensorFlow, Keras and scikit-learn. Farnood also joined TandemLaunch Inc. in 2020 as a Machine Learning Engineer. Prior to this, they worked at McGill University from 2017 to 2020 in various roles, including Graduate Researcher, Graduate Teaching Assistant, Undergraduate Researcher and Undergraduate Teaching Assistant. In their role as a Graduate Researcher, they worked on abstracts related to speech enhancement and time-frequency domain features. As an Undergraduate Researcher, they worked on graph signal processing and topology inference, as well as MIMO full-duplex FPGA testbed development for hardware verification of self-interference cancellation prototype. Farnood also worked as an Undergraduate Teaching Assistant, grading for MATH 381.

Farnood Faraji completed a Master of Science (Thesis) in Electrical and Computer Engineering from McGill University between 2018 and 2020. Prior to that, they obtained a Bachelor of Engineering - Honours in Electrical Engineering from McGill University between 2015 and 2017. Farnood also obtained a Bachelor of Engineering in Electrical Engineering from Sharif University of Technology between 2012 and 2015. Additionally, they obtained a Workplace Hazardous Materials Information System (WHMIS) certification from McGill University in May 2016.